WO2014143977A2

WO2014143977A2 - Biomarkers and methods for predicting preeclampsia

Info

Publication number: WO2014143977A2
Application number: PCT/US2014/028188
Authority: WO
Inventors: Durlin Edward HICKOK; John Jay BONIFACE; Gregory Charles CRITCHFIELD; Tracey Cristine FLEISCHER
Original assignee: Sera Prognostics, Inc.
Priority date: 2013-03-15
Filing date: 2014-03-14
Publication date: 2014-09-18
Also published as: EP4344705A2; US20190187145A1; EP2972393A4; US20190219588A1; CA3210007A1; EP3567371A1; EP2972393A2; AU2020201695A1; US20210156870A1; US20140287947A1; CA2907224C; AU2020201695B2; AU2022221441A1; CA2907224A1; US20140296108A1; AU2014228009A1; WO2014143977A3

Abstract

The disclosure provides biomarker panels, methods and kits for determining the probability for preeclampsia in a pregnant female. The present disclosure is based, in part, on the discovery that certain proteins and peptides in biological samples obtained from a pregnant female are differentially expressed in pregnant females that have an increased risk of developing in the future or presently suffering from preeclampsia relative to matched controls. The present disclosure is further based, in part, on the unexepected discovery that panels combining one or more of these proteins and peptides can be utilized in methods of determining the probability for preeclampsia in a pregnant female with relatively high sensitivity and specificity. These proteins and peptides dislosed herein serve as biomarkers for classifying test samples, predicting a probability of preeclampsia, monitoring of progress of preeclampsia in a pregnant female, either individually or in a panel of biomarkers.

Description

BIOMARKERS AND METHODS FOR PREDICTING PREECLAMPSIA

[0001] This application claims the benefit of priority to U.S. provisional patent application number 61/798,413, filed March 15, 2013, which is herein incorporated by reference in its entirety.

[0002] The invention relates generally to the field of personalized medicine and, more specifically to compositions and methods for determining the probability for preeclampsia in a pregnant female.

BACKGROUND

[0003] Preeclampsia (PE), a pregnancy-specific multi-system disorder characterized by hypertension and excess protein excretion in the urine, is a leading cause of maternal and fetal morbidity and mortality worldwide. Preeclampsia affects at least 5-8% of all pregnancies and accounts for nearly 18% of maternal deaths in the United States. The disorder is probably multifactorial, although most cases of preeclampsia are characterized by abnormal maternal uterine vascular remodeling by fetally derived placental trophoblast cells.

[0004] Complications of preeclampsia can include compromised placental blood flow, placental abruption, eclampsia, HELLP syndrome (hemolysis, elevated liver enzymes and low platelet count), acute renal failure, cerebral hemorrhage, hepatic failure or rupture, pulmonary edema, disseminated intravascular coagulation and future cardiovascular disease. Even a slight increase in blood pressure can be a sign of preeclampsia. While symptoms can include swelling, sudden weight gain, headaches and changes in vision, some women remain asymptomatic.

[0005] Management of preeclampsia consists of two options: delivery or observation. Management decisions depend on the gestational age at which preeclampsia is diagnosed and the relative state of health of the fetus . The only cure for preeclampsia is delivery of the fetus and placenta. However, the decision to deliver involves balancing the potential benefit to the fetus of further in utero development with fetal and maternal risk of progressive disease, including the development of eclampsia, which is preeclampsia complicated by maternal seizures. [0006] There is a great need to identify women at risk for preeclampsia as most currently available tests fail to predict the majority of women who eventually develop preeclampsia. Women identified as high-risk can be scheduled for more intensive antenatal surveillance and prophylactic interventions. Reliable early detection of preeclampsia would enable planning appropriate monitoring and clinical management, potentially providing the early

identification of disease complications. Such monitoring and management might include: more frequent assessment of blood pressure and urinary protein concentration, uterine artery doppler measurement, ultrasound assessment of fetal growth and prophylactic treatment with aspirin. Finally, reliable antenatal identification of preeclampsia also is crucial to cost- effective allocation of monitoring resources.

[0007] The present invention addresses this need by providing compositions and methods for determining whether a pregnant woman is at risk for developing preeclampsia. Related advantages are provided as well.

SUMMARY

[0008] The present invention provides compositions and methods for predicting the probability of preeclampsia in a pregnant female.

[0009] In one aspect, the invention provides a panel of isolated biomarkers comprising N of the biomarkers listed in Tables 2, 3, 4, 5 and 7 through 22. In some embodiments, N is a number selected from the group consisting of 2 to 24. In additional embodiments, the biomarker panel comprises at least two of the isolated biomarkers selected from the group consisting of FSVVYAK, SPELQAEAK, V HVTLSQPK, SSNNPHSPIVEEFQVPYNK, and VVGGLVALR. In additional embodiments, the biomarker panel comprises at least two of the isolated biomarkers selected from the group consisting of LDFHFSSDR,

TVQAVLTVPK, GPGEDFR, ETLLQDFR, ATVVYQGER, GFQALGDAADIR. In additional embodiments, the biomarker panel comprises at least two of the isolated biomarkers selected from the group consisting of FSVVYAK, SPELQAEAK,

VNHVTLSQPK, SSNNPHSPIVEEFQVPYNK, VVGGLVALR, LDFHFSSDR,

TVQAVLTVPK, GPGEDFR, ETLLQDFR, ATVVYQGER, and GFQALGDAADIR.

[0010] In some embodiments, the invention provides a biomarker panel comprising at least two of the isolated biomarkers selected from the group consisting of alpha- 1 - microglobulin (AMBP), ADP/ATP translocase 3 (ANT3), apolipoprotein A-II (APOA2), apolipoprotem B (APOB), apolipoprotem C-III (APOC3), beta-2-microglobulin (B2MG), complement component 1 , s subcomponent (C 1 S), and retinol binding protein 4 (RBP4 or RET4). In additional embodiments, the invention provides a biomarker panel comprising at least two isolated biomarkers selected from the group consisting of alpha- 1 -microglobulin (AMBP), ADP/ATP translocase 3 (ANT3), apolipoprotem A-II (APOA2), apolipoprotem B (APOB), apolipoprotem C-III (APOC3), beta-2-microglobulin (B2MG), complement component 1, s subcomponent (CI S), and retinol binding protein 4 (RBP4 or RET4).

[0011] In some embodiments, the invention provides a biomarker panel comprising at least two of the isolated biomarkers selected from the group consisting of Inhibin beta C chain (INHBC), Pigment epithelium-derived factor (PEDF), Prostaglandin-H2 D-isomerase (PTGDS), alpha- 1 -microglobulin (AMBP), Beta-2-glycoprotein 1 (APOH),

Metalloproteinase inhibitor 1 (TIMP1), Coagulation factor XIII B chain (F13B), Alpha-2- HS-glycoprotein (FETUA), Sex hormone-binding globulin (SHBG).

[0012] In other embodiments, the invention provides a biomarker panel comprising alpha- 1 -microglobulin (AMBP), ADP/ATP translocase 3 (ANT3), apolipoprotem A-II (APOA2), apolipoprotem B (APOB), apolipoprotem C-III (APOC3), beta-2-microglobulin (B2MG), complement component 1, subcomponent (CIS), and retinol binding protein 4 (RBP4 or RET4) cell adhesion molecule with homology to LI CAM (CHL1), complement component C5 (C5 or C05), complement component C8 beta chain (C8B or C08B), endothelin-converting enzyme 1 (ECE1), coagulation factor XIII, B polypeptide (F13B), interleukin 5 (IL5), Peptidase D (PEPD), and plasminogen (PLMN). In another aspect, the invention provides a biomarker panel comprising at least two isolated biomarkers selected from the group consisting of alpha- 1 -microglobulin (AMBP), ADP/ATP translocase 3 (ANT3), apolipoprotem A-II (APOA2), apolipoprotem B (APOB), apolipoprotein C-III (APOC3), beta-2-microglobulin (B2MG), complement component 1 , s subcomponent (CI S), and retinol binding protein 4 (RBP4 or RET4) cell adhesion molecule with homology to L1CAM (CHL1), complement component C5 (C5 or C05), complement component C8 beta chain (C8B or C08B), endothelin-converting enzyme 1 (ECE1), coagulation factor XIII, B polypeptide (F13B), interleukin 5 (IL5), Peptidase D (PEPD), and plasminogen (PLMN).

[0013] Also provided by the invention is a method of determining probability for preeclampsia in a pregnant female comprising detecting a measurable feature of each of N biomarkers selected from the biomarkers listed in Tables 2, 3, 4, 5 and 7 through 22 in a biological sample obtained from the pregnant female, and analyzing the measurable feature to determine the probability for preeclampsia in the pregnant female. In some embodiments, a measurable feature comprises fragments or derivatives of each of the N biomarkers selected from the biomarkers listed in Tables 2, 3, 4, 5 and 7 through 22. In some embodiments of the disclosed methods detecting a measurable feature comprises quantifying an amount of each of N biomarkers selected from the biomarkers listed in Tables 2, 3, 4, 5 and 7 through 22, combinations or portions and/or derivatives thereof in a biological sample obtained from the pregnant female. In additional embodiments, the disclosed methods of determining probability for preeclampsia in a pregnant female further encompass detecting a measurable feature for one or more risk indicia associated with preeclampsia.

[0014] In some embodiments, the disclosed methods of determining probability for preeclampsia in a pregnant female comprises detecting a measurable feature of each of N biomarkers, wherein N is selected from the group consisting of 2 to 24. In further embodiments, the disclosed methods of determining probability for preeclampsia in a pregnant female comprises detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of FSVVYAK, SPELQAEAK,

VNHVTLSQP , SSNNPHSPIVEEFQVPYNK, and VVGGLVALR.

[0015] In further embodiments, the disclosed methods of determining probability for preeclampsia in a pregnant female comprises detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of LDFHFSSDR,

TVQAVLTVPK, GPGEDFPv, ETLLQDFR, ATVVYQGER, GFQALGDAADIR.

[0016] In additional embodiments, the disclosed methods of determining probability for preeclampsia in a pregnant female comprises detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of FSVVYAK,

SPELQAEAK, VNHVTLSQPK, SSNNPHSPIVEEFQVPYNK, VVGGLVALR,

LDFHFSSDR, TVQAVLTVPK, GPGEDFR, ETLLQDFR, ATVVYQGER, and

GFQALGDAADIR.

[0017] In other embodiments, the disclosed methods of determining probability for preeclampsia in a pregnant female comprise detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of alpha- 1 -microglobulin (AMBP), ADP/ATP translocase 3 (ANT3), apolipoprotein A-II (APOA2), apolipoprotein B (APOB), apolipoprotein C-III (APOC3), beta-2-microglobulin (B2MG), complement component 1, s subcomponent (CI S), and retinol binding protein 4 (RBP4 or RET4).

[0018] In some embodiments, the disclosed methods of determining probability for preeclampsia in a pregnant female comprise detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of Inhibin beta C chain (INHBC), Pigment epithelium-derived factor (PEDF), Prostaglandin-H2 D-isomerase (PTGDS), alpha- 1 -microglobulin (AMBP), Beta-2-glycoprotein 1 (APOH), Metalloproteinase inhibitor 1 (TIMP1), Coagulation factor XIII B chain (F13B), Alpha-2-HS-glycoprotein (FETUA), Sex hormone-binding globulin (SHBG).

[0019] In further embodiments, the disclosed methods of determining probability for preeclampsia in a pregnant female comprise detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of alpha- 1 -microglobulin (AMBP), ADP/ATP translocase 3 (ANT3), apolipoprotein A-II (APOA2), apolipoprotein B (APOB), apolipoprotein C-III (APOC3), beta-2-microglobulin (B2MG), complement component 1, subcomponent (CIS), and retinol binding protein 4 (RBP4 or RET4) cell adhesion molecule with homology to LI CAM (CHL1), complement component C5 (C5 or C05), complement component C8 beta chain (C8B or C08B), endothelin-converting enzyme 1 (ECE1), coagulation factor XIII, B polypeptide (F13B), interleukin 5 (IL5), Peptidase D (PEPD), and plasminogen (PLMN).

[0020] In some embodiments of the methods of determining probability for preeclampsia in a pregnant female, the probability for preeclampsia in the pregnant female is calculated based on the quantified amount of each of N biomarkers selected from the biomarkers listed in Tables 2, 3, 4, 5 and 7 through 22. In some embodiments, the disclosed methods for determining the probability of preeclampsia encompass detecting and/or quantifying one or more biomarkers using mass sprectrometry, a capture agent or a combination thereof.

[0021] In some embodiments, the disclosed methods of determining probability for preeclampsia in a pregnant female encompass an initial step of providing a biomarker panel comprising N of the biomarkers listed in Tables 2, 3, 4, 5 and 7 through 22. In additional embodiments, the disclosed methods of determining probability for preeclampsia in a pregnant female encompass an initial step of providing a biological sample from the pregnant female. [0022] In some embodiments, the disclosed methods of determining probability for preeclampsia in a pregnant female encompass communicating the probability to a health care provider. In additional embodiments, the communication informs a subsequent treatment decision for the pregnant female. In further embodiments, the treatment decision comprises one or more selected from the group of consisting of more frequent assessment of blood pressure and urinary protein concentration, uterine artery doppler measurement, ultrasound assessment of fetal growth and prophylactic treatment with aspirin.

[0023] In further embodiments, the disclosed methods of determining probability for preeclampsia in a pregnant female encompass analyzing the measurable feature of one or more isolated biomarkers using a predictive model. In some embodiments of the disclosed methods, a measurable feature of one or more isolated biomarkers is compared with a reference feature.

[0024] In additional embodiments, the disclosed methods of determining probability for preeclampsia in a pregnant female encompass using one or more analyses selected from a linear discriminant analysis model, a support vector machine classification algorithm, a recursive feature elimination model, a prediction analysis of microarray model, a logistic regression model, a CART algorithm, a flex tree algorithm, a LART algorithm, a random forest algorithm, a MART algorithm, a machine learning algorithm, a penalized regression method, and a combination thereof. In one embodiment, the disclosed methods of determining probability for preeclampsia in a pregnant female encompasses logistic regression.

[0025] In some embodiments, the invention provides a method of determining probability for preeclampsia in a pregnant female encompasses quantifying in a biological sample obtained from the pregnant female an amount of each of N biomarkers selected from the biomarkers listed in Tables 2, 3, 4, 5 and 7 through 22; multiplying the amount by a predetermined coefficient, and determining the probability for preeclampsia in the pregnant female comprising adding the individual products to obtain a total risk score that corresponds to the probability.

[0026] Other features and advantages of the invention will be apparent from the detailed description, and from the claims. DETAILED DESCRIPTION

[0027] The present disclosure is based, in part, on the discovery that certain proteins and peptides in biological samples obtained from a pregnant female are differentially expressed in pregnant females that have an increased risk of developing in the future or presently suffering from preeclampsia relative to matched controls. The present disclosure is further based, in part, on the unexepected discovery that panels combining one or more of these proteins and peptides can be utilized in methods of determining the probability for preeclampsia in a pregnant female with relatively high sensitivity and specificity. These proteins and peptides dislosed herein serve as biomarkers for classifying test samples, predicting a probability of preeclampsia, monitoring of progress of preeclampsia in a pregnant female, either individually or in a panel of biomarkers.

[0028] The disclosure provides biomarker panels, methods and kits for determining the probability for preeclampsia in a pregnant female. One major advantage of the present disclosure is that risk of developing preeclampsia can be assessed early during pregnancy so that management of the condition can be initiated in a timely fashion. Sibai, Hypertension. In: Gabbe et ah, eds. Obstetrics: Normal and Problem Pregnancies. 6th ed. Philadelphia, Pa: Saunders Elsevier; 2012:chap 35. The present invention is of particular benefit to asymptomatic females who would not otherwise be identified and treated.

[0029] By way of example, the present disclosure includes methods for generating a result useful in determining probability for preeclampsia in a pregnant female by obtaining a dataset associated with a sample, where the dataset at least includes quantitative data about biomarkers and panels of biomarkers that have been identified as predictive of preeclampsia, and inputting the dataset into an analytic process that uses the dataset to generate a result useful in determining probability for preeclampsia in a pregnant female. As described further below, this quantitative data can include amino acids, peptides, polypeptides, proteins, nucleotides, nucleic acids, nucleosides, sugars, fatty acids, steroids, metabolites,

carbohydrates, lipids, hormones, antibodies, regions of interest that serve as surrogates for biological macromolecules and combinations thereof.

[0030] In addition to the specific biomarkers identified in this disclosure, for example, by accession number, sequence, or reference, the invention also contemplates contemplates use of biomarker variants that are at least 90% or at least 95% or at least 97% identical to the exemplified sequences and that are now known or later discover and that have utility for the methods of the invention. These variants may represent polymorphisms, splice variants, mutations, and the like. In this regard, the instant specification discloses multiple art-known proteins in the context of the invention and provides exemplary accession numbers associated with one or more public databases as well as exemplary references to published journal articles relating to these art-known proteins. However, those skilled in the art appreciate that additional accession numbers and journal articles can easily be identified that can provide additional characteristics of the disclosed biomarkers and that the exemplified references are in no way limiting with regard to the disclosed biomarkers. As described herein, various techniques and reagents find use in the methods of the present invention. Suitable samples in the context of the present invention include, for example, blood, plasma, serum, amniotic fluid, vaginal secretions, saliva, and urine. In some embodiments, the biological sample is selected from the group consisting of whole blood, plasma, and serum. In a particular embodiment, the biological sample is serum. As described herein, biomarkers can be detected through a variety of assays and techniques known in the art. As further described herein, such assays include, without limitation, mass spectrometry (MS)-based assays, antibody-based assays as well as assays that combine aspects of the two.

[0031] Protein biomarkers associated with the probability for preeclampsia in a pregnant female include, but are not limited to, one or more of the isolated biomarkers listed in Tables 2, 3, 4, 5, and 7 through 22. In addition to the specific biomarkers, the disclosure further includes biomarker variants that are about 90%, about 95%, or about 97% identical to the exemplified sequences. Variants, as used herein, include polymorphisms, splice variants, mutations, and the like.

[0032] Additional markers can be selected from one or more risk indicia, including but not limited to, maternal age, race, ethnicity, medical history, past pregnancy history, and obstetrical history. Such additional markers can include, for example, age, prepregnancy weight, ethnicity, race; the presence, absence or severity of diabetes, hypertension, heart disease, kidney disease; the incidence and/or frequency of prior preeclampsia, prior preeclampsia; the presence, absence, frequency or severity of present or past smoking, illicit drug use, alcohol use; the presence, absence or severity of bleeding after the 12th gestational week; cervical cerclage and transvaginal cervical length. Additional risk indicia useful for as markers can be identified using learning algorithms known in the art, such as linear discriminant analysis, support vector machine classification, recursive feature elimination, prediction analysis of microarray, logistic regression, CART, FlexTree, LART, random forest, MART, and/or survival analysis regression, which are known to those of skill in the art and are further described herein.

[0033] Provided herein are panels of isolated biomarkers comprising N of the biomarkers selected from the group listed in Tables 2, 3, 4, 5, and 7 through 22. In the disclosed panels of biomarkers N can be a number selected from the group consisting of 2 to 24. In the disclosed methods, the number of biomarkers that are detected and whose levels are determined, can be 1 , or more than 1 , such as 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 12, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25 or more. In certain embodiments, the number of biomarkers that are detected, and whose levels are determined, can be 1, or more than 1 , such as 2, 3, 4, 5, 6, 7, 8, 9, 10, or more. The methods of this disclosure are useful for determining the probability for preeclampsia in a pregnant female.

[0034] While certain of the biomarkers listed in Tables 2, 3, 4, 5, and 7 through 22 are useful alone for determining the probability for preeclampsia in a pregnant female, methods are also described herein for the grouping of multiple subsets of the biomarkers that are each useful as a panel of three or more biomarkers. In some embodiments, the invention provides panels comprising N biomarkers, wherein N is at least three biomarkers. In other

embodiments, N is selected to be any number from 3-23 biomarkers.

[0035] In yet other embodiments, N is selected to be any number from 2-5, 2-10, 2-15, 2- 20, or 2-23. In other embodiments, N is selected to be any number from 3-5, 3-10, 3-15, 3-20, or 3-23. In other embodiments, N is selected to be any number from 4-5, 4-10, 4-15, 4-20, or 4-23. In other embodiments, N is selected to be any number from 5-10, 5-15, 5-20, or 5-23. In other embodiments, N is selected to be any number from 6-10, 6-15, 6-20, or 6-23. In other embodiments, N is selected to be any number from 7-10, 7-15, 7-20, or 7-23. In other embodiments, N is selected to be any number from 8-10, 8-15, 8-20, or 8-23. In other embodiments, N is selected to be any number from 9-10, 9-15, 9-20, or 9-23. In other embodiments, N is selected to be any number from 10-15, 10-20, or 10-23. It will be appreciated that N can be selected to encompass similar, but higher order, ranges.

[0036] In certain embodiments, the panel of isolated biomarkers comprises one or more, two or more, three or more, four or more, or five isolated biomarkers comprising an amino acid sequence selected from SPELQAEAK, SSNNPHSPIVEEFQVPYN, VNHVTLSQP , VVGGLVALR, and FSVVYAK. In some embodiments, the panel of isolated biomarkers comprises one or more, two or more, three or more, four or more, five of the isolated biomarkers consisting of an amino acid sequence selected from SPELQAEAK,

SSNNPHSPIVEEFQVPYN, VNHVTLSQPK, VVGGLVALR, and FSVVYAK.

[0037] In certain embodiments, the panel of isolated biomarkers comprises one or more, two or more, three or more, four or more, or five isolated biomarkers comprising an amino acid sequence selected from LDFHFSSDR, TVQAVLTVPK, GPGEDFR, ETLLQDFR, ATVVYQGER, GFQALGDAADIR. In some embodiments, the panel of isolated biomarkers comprises one or more, two or more, three or more, four or more, five of the isolated biomarkers consisting of an amino acid sequence selected from LDFHFSSDR, TVQAVLTVPK, GPGEDFR, ETLLQDFR, ATVVYQGER, GFQALGDAADIR.

[0038] In certain embodiments, the panel of isolated biomarkers comprises one or more, two or more, three or more, four or more, or five isolated biomarkers comprising an amino acid sequence selected from FSVVYAK, SPELQAEAK, VNHVTLSQPK,

SSNNPHSPIVEEFQVPYNK, VVGGLVALR, LDFHFSSDR, TVQAVLTVPK,

GPGEDFR, ETLLQDFR, ATVVYQGER, and GFQALGDAADIR. In some embodiments, the panel of isolated biomarkers comprises one or more, two or more, three or more, four or more, five of the isolated biomarkers consisting of an amino acid sequence selected from FSVVYAK, SPELQAEAK, VNHVTLSQPK, SSNNPHSPIVEEFQVPYNK,

VVGGLVALR, LDFHFSSDR, TVQAVLTVPK, GPGEDFR, ETLLQDFR,

ATVVYQGER, and GFQALGDAADIR.

[0039] In some embodiments, the panel of isolated biomarkers comprises one or more peptides comprising a fragment from alpha- 1 -microglobulin (AMBP) Traboni and Cortese, Nucleic Acids Res. 14 (15), 6340 (1986); ADP/ATP translocase 3 (ANT3) Cozens et al , J. Mol. Biol. 206 (2), 261-280 (1989) (NCBI Reference Sequence: NP_001627.2);

apolipoprotein A-II (APOA2) Fullerton et al , Hum. Genet. 1 1 1 (1), 75-87 (2002) GenBank: AY100524.1); apolipoprotein B (APOB) Knott et al, Nature 323, 734 - 738 (1986) (GenBank: EAX00803.1); apolipoprotein C-III (APOC3), Fullerton et al, Hum. Genet. 1 15 (1), 36-56 (2004)(GenBank: AAS68230.1); beta-2-microglobulin (B2MG) Cunningham et al , Biochemistry 12 (24), 4811-4822 (1973) (GenBank: AI686916.1); complement component 1, s subcomponent (CI S) Mackinnon et al , Eur. J. Biochem. 169 (3), 547-553 (1987), and retinol binding protein 4 (RBP4 or RET4) Rask et al , Ann. N. Y. Acad. Sci. 359, 79-90 (1981) (UniProtKB/Swiss-Prot: P02753.3).

[0040] In some embodiments, the panel of isolated biomarkers comprises one or more peptides comprising a fragment from cell adhesion molecule with homology to LI CAM (close homolog of LI) (CHL1) (GenBank: AAI43497.1), complement component C5 (C5 or C05) Haviland, J. Immunol. 146 (1), 362-368 (1991)( GenBank: AAA51925.1);

Complement component C8 beta chain (C8B or C08B) Howard et al, Biochemistry 26 (12), 3565-3570 (1987) (NCBI Reference Sequence: NP_000057.1), endothelin-converting enzyme 1 (ECE1) Xu et al, Cell 78 (3), 473-485 (1994) (NCBI Reference Sequence:

NM_001397.2; NP_001388.1); coagulation factor XIII, B polypeptide (F13B) Grundmann et al . Nucleic Acids Res. 18 (9), 2817-2818 (1990) (NCBI Reference Sequence:

NP_001985.2); Interleukin 5 (IL5), Murata et al, J. Exp. Med. 175 (2), 341-351 (1992) (NCBI Reference Sequence: NP_000870.1), Peptidase D (PEPD) Endo et al, J. Biol. Chem. 264 (8), 4476-4481 (1989) (UniProtKB/Swiss-Prot: P12955.3); Plasminogen (PLMN) Petersen et al, J. Biol. Chem. 265 (11), 6104-61 11 (1990), (NCBI Reference Sequences: NP_000292.1 NP_001161810.1).

[0041] In additional embodiments, the invention provides a panel of isolated biomarkers comprising N of the biomarkers listed in Tables 2, 3, 4, 5 and 7 through 22. In some embodiments, N is a number selected from the group consisting of 2 to 24. In additional embodiments, the biomarker panel comprises at least two of the isolated biomarkers selected from the group consisting of FSVVYAK, SPELQAEA , VNHVTLSQP ,

SSNNPHSPIVEEFQVPYNK, and VVGGLVALR.

[0042] In further embodiments, the biomarker panel comprises at least two of the isolated biomarkers selected from the group consisting of alpha- 1 -microglobulin (AMBP), ADP/ATP translocase 3 (ANT3), apolipoprotein A-II (APOA2), apolipoprotein B (APOB),

apolipoprotein C-III (APOC3), beta-2-microglobulin (B2MG), complement component 1 , s subcomponent (CIS), and retinol binding protein 4 (RBP4 or RET4). In another

embodiment, the invention provides a biomarker panel comprising at least three isolated biomarkers selected from the group consisting of alpha- 1 -microglobulin (AMBP), ADP/ATP translocase 3 (ANT3), apolipoprotein A-II (APOA2), apolipoprotein B (APOB),

apolipoprotein C-III (APOC3), beta-2-microglobulin (B2MG), complement component 1 , s subcomponent (CIS), and retinol binding protein 4 (RBP4 or RET4). [0043] In further embodiments, the biomarker panel comprises at least two of the isolated biomarkers selected from the group consisting Inhibin beta C chain (INHBC), Pigment epithelium-derived factor (PEDF), Prostaglandin-H2 D-isomerase (PTGDS), alpha- 1- microglobulin (AMBP), Beta-2-glycoprotein 1 (APOH), Metalloproteinase inhibitor 1 (TIMPl), Coagulation factor XIII B chain (F13B), Alpha-2-HS-glycoprotein (FETUA), Sex hormone-binding globulin (SHBG). In another embodiment, the invention provides a biomarker panel comprising at least three isolated biomarkers selected from the group consisting of Inhibin beta C chain (INHBC), Pigment epithelium-derived factor (PEDF), Prostaglandin-H2 D-isomerase (PTGDS), alpha- 1 -microglobulin (AMBP), Beta-2- glycoprotein 1 (APOH), Metalloproteinase inhibitor 1 (TIMPl), Coagulation factor XIII B chain (F13B), Alpha-2-HS-glycoprotein (FETUA), Sex hormone-binding globulin (SHBG).

[0044] In some embodiments, the invention provides a biomarker panel comprising alpha- 1 -microglobulin (AMBP), ADP/ATP translocase 3 (ANT3), apolipoprotein A-II (APOA2), apolipoprotein B (APOB), apolipoprotein C-III (APOC3), beta-2-microglobulin (B2MG), complement component 1, subcomponent (CIS), and retinol binding protein 4 (RBP4 or RET4) cell adhesion molecule with homology to LICAM (CHLl), complement component C5 (C5 or C05), complement component C8 beta chain (C8B or C08B), endothelin-converting enzyme 1 (ECE1), coagulation factor XIII, B polypeptide (F13B), interleukin 5 (IL5), Peptidase D (PEPD), and plasminogen (PLMN). In another aspect, the invention provides a biomarker panel comprising at least two isolated biomarkers selected from the group consisting of alpha- 1 -microglobulin (AMBP), ADP/ATP translocase 3 (ANT3), apolipoprotein A-II (APOA2), apolipoprotein B (APOB), apolipoprotein C-III (APOC3), beta-2-microglobulin (B2MG), complement component 1 , s subcomponent (CI S), and retinol binding protein 4 (RBP4 or RET4) cell adhesion molecule with homology to LICAM (CHLl), complement component C5 (C5 or C05), complement component C8 beta chain (C8B or C08B), endothelin-converting enzyme 1 (ECE1), coagulation factor XIII, B polypeptide (F13B), interleukin 5 (IL5), Peptidase D (PEPD), and plasminogen (PLMN).

[0045] In some embodiments, the invention provides a biomarker panel comprising Inhibin beta C chain (INHBC), Pigment epithelium-derived factor (PEDF), Prostaglandin-H2 D-isomerase (PTGDS), alpha- 1 -microglobulin (AMBP), Beta-2-glycoprotein 1 (APOH), Metalloproteinase inhibitor 1 (TIMPl), Coagulation factor XIII B chain (F13B), Alpha-2- HS-glycoprotein (FETUA), Sex hormone-binding globulin (SHBG). In another aspect, the invention provides a biomarker panel comprising at least two isolated biomarkers selected from the group consisting of Inhibin beta C chain (INHBC), Pigment epithelium-derived factor (PEDF), Prostaglandin-^ D-isomerase (PTGDS), alpha- 1 -microglobulin (AMBP), Beta-2-glycoprotein 1 (APOH), Metalloproteinase inhibitor 1 (TIMP1), Coagulation factor XIII B chain (F13B), Alpha-2-HS-glycoprotein (FETUA), Sex hormone-binding globulin (SHBG).

[0046] As used in this application, including the appended claims, the singular forms "a," "an," and "the" include plural references, unless the content clearly dictates otherwise, and are used interchangeably with "at least one" and "one or more."

[0047] The term "about," particularly in reference to a given quantity, is meant to encompass deviations of plus or minus five percent.

[0048] As used herein, the terms "comprises," "comprising," "includes," "including," "contains," "containing," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, product-by-process, or composition of matter that comprises, includes, or contains an element or list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, product-by-process, or composition of matter.

[0049] As used herein, the term "panel" refers to a composition, such as an array or a collection, comprising one or more biomarkers. The term can also refer to a profile or index of expression patterns of one or more biomarkers described herein. The number of biomarkers useful for a biomarker panel is based on the sensitivity and specificity value for the particular combination of biomarker values.

[0050] As used herein, and unless otherwise specified, the terms "isolated" and "purified" generally describes a composition of matter that has been removed from its native environment (e.g., the natural environment if it is naturally occurring), and thus is altered by the hand of man from its natural state. An isolated protein or nucleic acid is distinct from the way it exists in nature.

[0051] The term "biomarker" refers to a biological molecule, or a fragment of a biological molecule, the change and/or the detection of which can be correlated with a particular physical condition or state. The terms "marker" and "biomarker" are used interchangeably throughout the disclosure. For example, the biomarkers of the present invention are correlated with an increased likelihood of preeclampsia. Such biomarkers include, but are not limited to, biological molecules comprising nucleotides, nucleic acids, nucleosides, amino acids, sugars, fatty acids, steroids, metabolites, peptides, polypeptides, proteins, carbohydrates, lipids, hormones, antibodies, regions of interest that serve as surrogates for biological macromolecules and combinations thereof (e.g., glycoproteins, ribonucleoproteins, lipoproteins). The term also encompasses portions or fragments of a biological molecule, for example, peptide fragment of a protein or polypeptide that comprises at least 5 consecutive amino acid residues, at least 6 consecutive amino acid residues, at least 7 consecutive amino acid residues, at least 8 consecutive amino acid residues, at least 9 consecutive amino acid residues, at least 10 consecutive amino acid residues, at least 11 consecutive amino acid residues, at least 12 consecutive amino acid residues, at least 13 consecutive amino acid residues, at least 14 consecutive amino acid residues, at least 15 consecutive amino acid residues, at least 5 consecutive amino acid residues, at least 16 consecutive amino acid residues, at least 17consecutive amino acid residues, at least 18 consecutive amino acid residues, at least 19 consecutive amino acid residues, at least 20 consecutive amino acid residues, at least 21 consecutive amino acid residues, at least 22 consecutive amino acid residues, at least 23 consecutive amino acid residues, at least 24 consecutive amino acid residues, at least 25 consecutive amino acid residues,or more consecutive amino acid residues.

[0052] The invention also provides a method of determining probability for preeclampsia in a pregnant female, the method comprising detecting a measurable feature of each of N biomarkers selected from the biomarkers listed in Tables 2, 3, 4, 5 and 7 through 22 in a biological sample obtained from the pregnant female, and analyzing the measurable feature to determine the probability for preeclampsia in the pregnant female. As disclosed herein, a measurable feature comprises fragments or derivatives of each of said N biomarkers selected from the biomarkers listed in Tables 2, 3, 4, 5 and 7 through 22. In some embodiments of the disclosed methods detecting a measurable feature comprises quantifying an amount of each of N biomarkers selected from the biomarkers listed in Tables 2, 3, 4, 5 and 7 through 22, combinations or portions and/or derivatives thereof in a biological sample obtained from said pregnant female. [0053] In some embodiments, the present invention describes a method for predicting the time to onset of preeclamspsia in a pregnant female, the method comprising: (a) obtaining a biological sample from said pregnant female; (b) quantifying an amount of each of N biomarkers selected from the biomarkers listed in Tables 2, 3, 4, 5 and 7 through 22 in said biological sample; (c) multiplying or thresholding said amount by a predetermined coefficient, (d) determining predicted onset of of said preeclampsia in said pregnant female comprising adding said individual products to obtain a total risk score that corresponds to said predicted onset of said preeclampsia in said pregnant female. Although described and exemplified with reference to methods of determining probability for preeclampsia in a pregnant female, the present disclosure is similarly applicable to the method of predicting time to onset of in a pregnant female. It will be apparent to one skilled in the art that each of the aforementioned methods has specific and substantial utilities and benefits with regard maternal-fetal health considerations.

[0054] In some embodiments, the method of determining probability for preeclampsia in a pregnant female comprises detecting a measurable feature of each of N biomarkers, wherein N is selected from the group consisting of 2 to 24. In further embodiments, the disclosed methods of determining probability for preeclampsia in a pregnant female comprises detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of FSVVYA , SPELQAEA , VNHVTLSQP ,

SSNNPHSPIVEEFQVPYNK, and VVGGLVALR.

[0055] In further embodiments, the disclosed methods of determining probability for preeclampsia in a pregnant female comprises detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of LDFHFSSDR,

TVQAVLTVPK, GPGEDFPv, ETLLQDFR, ATVVYQGER, GFQALGDAADIR.

[0056] In further embodiments, the disclosed methods of determining probability for preeclampsia in a pregnant female comprises detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of FSVVYAK,

SPELQAEAK, VNHVTLSQPK, SSNNPHSPIVEEFQVPYNK, VVGGLVALR,

LDFHFSSDR, TVQAVLTVPK, GPGEDFR, ETLLQDFR, ATVVYQGER, and

GFQALGDAADIR [0057] In additional embodiments, the method of determining probability for preeclampsia in a pregnant female comprises detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of alpha- 1 -microglobulin (AMBP), ADP/ATP translocase 3 (ANT3), apolipoprotem A-II (APOA2), apolipoprotem B (APOB), apolipoprotein C-III (APOC3), beta-2-microglobulin (B2MG), complement component 1, s subcomponent (CI S), and retinol binding protein 4 (RBP4 or RET4).

[0058] In additional embodiments, the method of determining probability for preeclampsia in a pregnant female comprises detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of Inhibin beta C chain (INHBC), Pigment epithelium-derived factor (PEDF), Prostaglandin-H2 D-isomerase (PTGDS), alpha- 1 -microglobulin (AMBP), Beta-2-glycoprotein 1 (APOH),

[0059] In further embodiments, the disclosed method of determining probability for preeclampsia in a pregnant female comprises detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of alpha- 1 -microglobulin (AMBP), ADP/ATP translocase 3 (ANT3), apolipoprotein A-II (APOA2), apolipoprotein B (APOB), apolipoprotein C-III (APOC3), beta-2-microglobulin (B2MG), complement component 1, subcomponent (CIS), and retinol binding protein 4 (RBP4 or RET4) cell adhesion molecule with homology to LI CAM (CHL1), complement component C5 (C5 or C05), complement component C8 beta chain (C8B or C08B), endothelin-converting enzyme 1 (ECE1), coagulation factor XIII, B polypeptide (F13B), interleukin 5 (IL5), Peptidase D (PEPD), plasminogen (PLMN), of Inhibin beta C chain (INHBC), Pigment epithelium- derived factor (PEDF), Prostaglandin-H2 D-isomerase (PTGDS), alpha- 1 -microglobulin (AMBP), Beta-2-glycoprotein 1 (APOH), Metalloproteinase inhibitor 1 (TIMP1),

Coagulation factor XIII B chain (F13B), Alpha-2-HS-glycoprotein (FETUA), Sex hormone- binding globulin (SHBG).

[0060] In additional embodiments, the methods of determining probability for preeclampsia in a pregnant female further encompass detecting a measurable feature for one or more risk indicia associated with preeclampsia. In additional embodiments the risk indicia are selected form the group consisting of history of preeclampsia, first pregnancy, age, obesity, diabetes, gestational diabetes, hypertension, kidney disease, multiple pregnancy, interval between pregnancies, migraine headaches, rheumatoid arthritis, and lupus.

[0061] A "measurable feature" is any property, characteristic or aspect that can be determined and correlated with the probability for preeclampsia in a subject. For a biomarker, such a measurable feature can include, for example, the presence, absence, or concentration of the biomarker, or a fragment thereof, in the biological sample, an altered structure, such as, for example, the presence or amount of a post-translational modification, such as oxidation at one or more positions on the amino acid sequence of the biomarker or, for example, the presence of an altered conformation in comparison to the conformation of the biomarker in normal control subjects, and/or the presence, amount, or altered structure of the biomarker as a part of a profile of more than one biomarker. In addition to biomarkers, measurable features can further include risk indicia including, for example, maternal age, race, ethnicity, medical history, past pregnancy history, obstetrical history. For a risk indicium, a measurable feature can include, for example, age, prepregnancy weight, ethnicity, race; the presence, absence or severity of diabetes, hypertension, heart disease, kidney disease; the incidence and/or frequency of prior preeclampsia, prior preeclampsia; the presence, absence, frequency or severity of present or past smoking, illicit drug use, alcohol use; the presence, absence or severity of bleeding after the 12th gestational week; cervical cerclage and transvaginal cervical length.

[0062] In some embodiments of the disclosed methods of determining probability for preeclampsia in a pregnant female, the probability for preeclampsia in the pregnant female is calculated based on the quantified amount of each of N biomarkers selected from the biomarkers listed in Tables 2, 3, 4, 5 and 7 through 22. In some embodiments, the disclosed methods for determining the probability of preeclampsia encompass detecting and/or quantifying one or more biomarkers using mass sprectrometry, a capture agent or a combination thereof.

[0063] In some embodiments, the disclosed methods of determining probability for preeclampsia in a pregnant female encompass an initial step of providing a biomarker panel comprising N of the biomarkers listed in Tables 2, 3, 4, 5 and 7 through 22. In additional embodiments, the disclosed methods of determining probability for preeclampsia in a pregnant female encompass an initial step of providing a biological sample from the pregnant female. [0064] In some embodiments, the disclosed methods of determining probability for preeclampsia in a pregnant female encompass communicating the probability to a health care provider. In additional embodiments, the communication informs a subsequent treatment decision for the pregnant female.

[0065] In some embodiments, the method of determining probability for preeclampsia in a pregnant female encompasses the additional feature of expressing the probability as a risk score.

[0066] As used herein, the term "risk score" refers to a score that can be assigned based on comparing the amount of one or more biomarkers in a biological sample obtained from a pregnant female to a standard or reference score that represents an average amount of the one or more biomarkers calculated from biological samples obtained from a random pool of pregnant females. Because the level of a biomarker may not be static throughout pregnancy, a standard or reference score has to have been obtained for the gestational time point that corresponds to that of the pregnant female at the time the sample was taken. The standard or reference score can be predetermined and built into a predictor model such that the comparison is indirect rather than actually performed every time the probability is determined for a subject. A risk score can be a standard (e.g., a number) or a threshold (e.g., a line on a graph). The value of the risk score correlates to the deviation, upwards or downwards, from the average amount of the one or more biomarkers calculated from biological samples obtained from a random pool of pregnant females. In certain embodiments, if a risk score is greater than a standard or reference risk score, the pregnant female can have an increased likelihood of preeclampsia. In some embodiments, the magnitude of a pregnant female's risk score, or the amount by which it exceeds a reference risk score, can be indicative of or correlated to that pregnant female's level of risk.

[0067] In the context of the present invention, the term "biological sample," encompasses any sample that is taken from pregnant female and contains one or more of the biomarkers listed in Table 1. Suitable samples in the context of the present invention include, for example, blood, plasma, serum, amniotic fluid, vaginal secretions, saliva, and urine. In some embodiments, the biological sample is selected from the group consisting of whole blood, plasma, and serum. As will be appreciated by those skilled in the art, a biological sample can include any fraction or component of blood, without limitation, T cells, monocytes, neutrophils, erythrocytes, platelets and microvesicles such as exosomes and exosome-like vesicles. In a particular embodiment, the biological sample is serum.

[0068] Preeclampsia refers to a condition characterized by high blood pressure and excess protein in the urine (proteinuria) after 20 weeks of pregnancy in a woman who previously had normal blood pressure. Preeclampsia encompasses Eclampsia, a more severe form of preeclampsia that is further characterized by seizures. Preeclampsia can be further classified as mild or severe depending upon the severity of the clinical symptoms. While preeclampsia usually develops during the second half of pregnancy (after 20 weeks), it also can develop shortly after birth or before 20 weeks of pregnancy.

[0069] Preeclampsia has been characterized by some investigators as 2 different disease entities: early-onset preeclampsia and late-onset preeclampsia, both of which are intended to be encompassed by reference to preeclampsia herein. Early-onset preeclampsia is usually defined as preeclampsia that develops before 34 weeks of gestation, whereas late-onset preeclampsia develops at or after 34 weeks of gestation. Preclampsia also includes postpartum preeclampsia is a less common condition that occurs when a woman has high blood pressure and excess protein in her urine soon after childbirth. Most cases of postpartum preeclampsia develop within 48 hours of childbirth. However, postpartum preeclampsia sometimes develops up to four to six weeks after childbirth. This is known as late postpartum preeclampsia.

[0070] Clinical criteria for diagnosis of preeclampsia are well established, for example, blood pressure of at least 140/90 mm Hg and urinary excretion of at least 0.3 grams of protein in a 24-hour urinary protein excretion (or at least +1 or greater on dipstick testing), each on two occasions 4-6 hours apart. Severe preeclampsia generally refers to a blood pressure of at least 160/110 mm Hg on at least 2 occasions 6 hours apart and greater than 5 grams of protein in a 24-hour urinary protein excretion or persistent +3 proteinuria on dipstick testing.

Preeclampsia can include HELLP syndrome (hemolysis, elevated liver enzymes, low platelet count). Other elements of preeclampsia can include in-utero growth restriction (IUGR) in less than the 10% percentile according to the US demographics, persistent neurologic symptoms (headache, visual disturbances), epigastric pain, oliguria (less than 500 mL/24 h), serum creatinine greater than 1.0 mg/dL, elevated liver enzymes (greater than two times normal), thrombocytopenia (< 100,000 cells/ 'μΚ). [0071] In some embodiments, the pregnant female was between 17 and 28 weeks of gestation at the time the biological sample was collected. In other embodiments, the pregnant female was between 16 and 29 weeks, between 17 and 28 weeks, between 18 and 27 weeks, between 19 and 26 weeks, between 20 and 25 weeks, between 21 and 24 weeks, or between 22 and 23 weeks of gestation at the time the biological sample was collected. In further embodiments, the the pregnant female was between about 17 and 22 weeks, between about 16 and 22 weeks between about 22 and 25 weeks, between about 13 and 25 weeks, between about 26 and 28, or between about 26 and 29 weeks of gestation at the time the biological sample was collected. Accordingly, the gestational age of a pregnant female at the time the biological sample is collected can be 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29 or 30 weeks.

[0072] In some embodiments of the claimed methods the measurable feature comprises fragments or derivatives of each of the N biomarkers selected from the biomarkers listed in Table 1. In additional embodiments of the claimed methods, detecting a measurable feature comprises quantifying an amount of each of N biomarkers selected from the biomarkers listed in Table 1 , combinations or portions and/or derivatives thereof in a biological sample obtained from said pregnant female.

[0073] The term "amount" or "level" as used herein refers to a quantity of a biomarker that is detectable or measurable in a biological sample and/or control. The quantity of a biomarker can be, for example, a quantity of polypeptide, the quantity of nucleic acid, or the quantity of a fragment or surrogate. The term can alternatively include combinations thereof. The term "amount" or "level" of a biomarker is a measurable feature of that biomarker.

[0074] In some embodiments, calculating the probability for preeclampsia in a pregnant female is based on the quantified amount of each of N biomarkers selected from the biomarkers listed in Table 1. Any existing, available or conventional separation, detection and quantification methods can be used herein to measure the presence or absence (e.g., readout being present vs. absent; or detectable amount vs. undetectable amount) and/or quantity (e.g., readout being an absolute or relative quantity, such as, for example, absolute or relative concentration) of biomarkers, peptides, polypeptides, proteins and/or fragments thereof and optionally of the one or more other biomarkers or fragments thereof in samples. In some embodiments, detection and/or quantification of one or more biomarkers comprises an assay that utilizes a capture agent. In further ambodiments, the capture agent is an antibody, antibody fragment, nucleic acid-based protein binding reagent, small molecule or variant thereof. In additional embodiments, the assay is an enzyme immunoassay (EIA), enzyme-linked immunosorbent assay (ELISA), and radioimmunoassay (RIA). In some embodiments, detection and/or quantification of one or more biomarkers further comprises mass spectrometry (MS). In yet further embodiments, the mass spectrometry is co- immunoprecitipation-mass spectrometry (co-IP MS), where coimmunoprecipitation, a technique suitable for the isolation of whole protein complexes is followed by mass spectrometric analysis.

[0075] As used herein, the term "mass spectrometer" refers to a device able to volatilize/ionize analytes to form gas-phase ions and determine their absolute or relative molecular masses. Suitable methods of volatilization/ionization are matrix-assisted laser desorption ionization (MALDI), electrospray, laser/light, thermal, electrical,

atomized/sprayed and the like, or combinations thereof. Suitable forms of mass spectrometry include, but are not limited to, ion trap instruments, quadrupole instruments, electrostatic and magnetic sector instruments, time of flight instruments, time of flight tandem mass spectrometer (TOF MS/MS), Fourier-transform mass spectrometers, Orbitraps and hybrid instruments composed of various combinations of these types of mass analyzers. These instruments can, in turn, be interfaced with a variety of other instruments that fractionate the samples (for example, liquid chromatography or solid-phase adsorption techniques based on chemical, or biological properties) and that ionize the samples for introduction into the mass spectrometer, including matrix-assisted laser desorption (MALDI), electrospray, or nanospray ionization (ESI) or combinations thereof.

[0076] Generally, any mass spectrometric (MS) technique that can provide precise information on the mass of peptides, and preferably also on fragmentation and/or (partial) amino acid sequence of selected peptides (e.g., in tandem mass spectrometry, MS/MS; or in post source decay, TOF MS), can be used in the methods disclosed herein. Suitable peptide MS and MS/MS techniques and systems are well-known per se (see, e.g., Methods in Molecular Biology, vol. 146: "Mass Spectrometry of Proteins and Peptides", by Chapman, ed., Humana Press 2000; Biemann 1990. Methods Enzymol 193 : 455-79; or Methods in Enzymology, vol. 402: "Biological Mass Spectrometry", by Burlingame, ed., Academic Press 2005) and can be used in practicing the methods disclosed herein. Accordingly, in some embodiments, the disclosed methods comprise performing quantitative MS to measure one or more biomarkers. Such quantitiative methods can be performed in an automated (Villanueva, et ah, Nature Protocols (2006) 1(2):880-891) or semi-automated format. In particular embodiments, MS can be operably linked to a liquid chromatography device (LC-MS/MS or LC-MS) or gas chromatography device (GC-MS or GC-MS/MS). Other methods useful in this context include isotope-coded affinity tag (ICAT) followed by chromatography and MS/MS.

[0077] As used herein, the terms "multiple reaction monitoring (MRM)" or "selected reaction monitoring (SRM)" refer to an MS-based quantification method that is particularly useful for quantifying analytes that are in low abundance. In an SRM experiment, a predefined precursor ion and one or more of its fragments are selected by the two mass filters of a triple quadrupole instrument and monitored over time for precise quantification. Multiple SRM precursor and fragment ion pairs cars be measured within the same experiment on the chromatographic time scale by rapidly toggling between the different precursor/fragment pairs to perform an MRM experiment . A series of transitions (precursor/fragment ion pairs) in combination with the retention time of the targeted analyte {e.g. , peptide or small molecule such as chemical entity, steroid, hormone) can constitute a definitive assay. A large number of analytes can be quantified during a single LC-MS experiment. The term "scheduled," or "dynamic" in reference to MRM or SRM, refers to a variation of the assay wherein the transitions for a particular analyte are only acquired in a time window around the expected retention time, significantly increasing the number of analytes that can be detected and quantified in a single LC-MS experiment and contributing to the selectivity of the test, as retention time is a property dependent on the physical nature of the analyte. A single analyte can also be monitored with more than one transition. Finally, included in the assay can be standards that correspond to the analytes of interest {e.g., same amino acid sequence), but differ by the inclusion of stable isotopes. Stable isotopic standards (SIS) can be incorporated into the assay at precise levels and used to quantify the corresponding unknown analyte. An additional level of specificity is contributed by the co-elution of the unknown analyte and its corresponding SIS and properties of their transitions {e.g. , the similarity in the ratio of the level of two transitions of the unknown and the ratio of the two transitions of its

corresponding SIS).

[0078] Mass spectrometry assays, instruments and systems suitable for biomarker peptide analysis can include, without limitation, matrix-assisted laser desorption/ionisation time-of- flight (MALDI-TOF) MS; MALDI-TOF post-source-decay (PSD); MALDI-TOF/TOF; surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF) MS; electrospray ionization mass spectrometry (ESI-MS); ESI-MS/MS; ESI-MS/(MS)_n (n is an integer greater than zero); ESI 3D or linear (2D) ion trap MS; ESI triple quadrupole MS; ESI quadrupole orthogonal TOF (Q-TOF); ESI Fourier transform MS systems;

desorption/ionization on silicon (DIOS); secondary ion mass spectrometry (SIMS);

atmospheric pressure chemical ionization mass spectrometry (APCI-MS); APCI-MS MS; APCI- (MS)_n; atmospheric pressure photoionization mass spectrometry (APPI-MS); APPI- MS/MS; and APPI- (MS)„. Peptide ion fragmentation in tandem MS (MS/MS) arrangements can be achieved using manners established in the art, such as, e.g., collision induced dissociation (CID). As described herein, detection and quantification of biomarkers by mass spectrometry can involve multiple reaction monitoring (MRM), such as described among others by Kuhn et al. Proteomics 4: 1 175-86 (2004). Scheduled multiple-reaction-monitoring (Scheduled MRM) mode acquisition during LC-MS/MS analysis enhances the sensitivity and accuracy of peptide quantitation. Anderson and Hunter , Molecular and Cellular

Proteomics 5(4):573 (2006). As described herein, mass spectrometry-based assays can be advantageously combined with upstream peptide or protein separation or fractionation methods, such as for example with the chromatographic and other methods described herein below.

[0079] A person skilled in the art will appreciate that a number of methods can be used to determine the amount of a biomarker, including mass spectrometry approaches, such as MS/MS, LC-MS/MS, multiple reaction monitoring (MRM) or SRM and product-ion monitoring (PIM) and also including antibody based methods such as immunoassays such as Western blots, enzyme-linked immunosorbant assay (ELISA), immunopercipitation, immunohistochemistry, immunofluorescence, radioimmunoassay, dot blotting, and fluorescence-activated cell sorting (FACS). Accordingly, in some embodiments, determining the level of the at least one biomarker comprises using an immunoassay and/or mass spectrometric methods. In additional embodiments, the mass spectrometric methods are selected from MS, MS/MS, LC-MS/MS, SRM, PIM, and other such methods that are known in the art. In other embodiments, LC-MS/MS further comprises ID LC-MS/MS, 2D LC- MS/MS or 3D LC-MS/MS. Immunoassay techniques and protocols are generally known to those skilled in the art ( Price and Newman, Principles and Practice of Immunoassay, 2nd Edition, Grove's Dictionaries, 1997; and Gosling, Immunoassays: A Practical Approach, Oxford University Press, 2000.) A variety of immunoassay techniques, including

competitive and non-competitive immunoassays, can be used ( Self et al, Curr. Opin.

BiotechnoL. 7:60-65 (1996).

[0080] In further embodiments, the immunoassay is selected from Western blot, ELISA, immunopercipitation, immunohistochemistry, immunofluorescence, radioimmunoassay (RIA), dot blotting, and FACS. In certain embodiments, the immunoassay is an ELISA. In yet a further embodiment, the ELISA is direct ELISA (enzyme-linked immunosorbent assay), indirect ELISA, sandwich ELISA, competitive ELISA, multiplex ELISA, ELISPOT technologies, and other similar techniques known in the art. Principles of these immunoassay methods are known in the art, for example John R. Crowther, The ELISA Guidebook. 1st ed., Humana Press 2000, ISBN 0896037282. Typically ELISAs are performed with antibodies but they can be performed with any capture agents that bind specifically to one or more biomarkers of the invention and that can be detected. Multiplex ELISA allows simultaneous detection of two or more analytes within a single compartment (e.g., microplate well) usually at a plurality of array addresses (Nielsen and Geierstanger 2004. J Immunol Methods 290: 107-20 (2004) and Ling et al. 2007. Expert Rev Mol Diagn 7: 87-98 (2007)).

[0081] In some embodiments, Radioimmunoassay (RIA) can be used to detect one or more biomarkers in the methods of the invention. RIA is a competition-based assay that is well known in the art and involves mixing known quantities of radioactavely-labelled (e.g.,¹²⁵I or ¹³¹I-labelled) target analyte with antibody specific for the analyte, then adding non-labelled analyte from a sample and measuring the amount of labelled analyte that is displaced (see, e.g., An Introduction to Radioimmunoassay and Related Techniques, by Chard T, ed., Elsevier Science 1995, ISBN 0444821 198 for guidance).

[0082] A detectable label can be used in the assays described herein for direct or indirect detection of the biomarkers in the methods of the invention. A wide variety of detectable labels can be used, with the choice of label depending on the sensitivity required, ease of conjugation with the antibody, stability requirements, and available instrumentation and disposal provisions. Those skilled in the art are familiar with selection of a suitable detectable label based on the assay detection of the biomarkers in the methods of the invention. Suitable detectable labels include, but are not limited to, fluorescent dyes (e.g., fluorescein, fluorescein isothiocyanate (FITC), Oregon Green™, rhodamine, Texas red, tetrarhodimine isothiocynate (TRITC), Cy3, Cy5, etc.), fluorescent markers (e.g., green fluorescent protein (GFP), phycoerythrin, etc.), enzymes (e.g., luciferase, horseradish peroxidase, alkaline phosphatase, etc.), nanoparticles, biotin, digoxigenin, metals, and the like.

[0083] For mass-sectrometry based analysis, differential tagging with isotopic reagents, e.g., isotope-coded affinity tags (ICAT) or the more recent variation that uses isobaric tagging reagents, iTRAQ (Applied Biosystems, Foster City, Calif.), or tandem mass tags, TMT, (Thermo Scientific, ockford, IL), followed by multidimensional liquid chromatography (LC) and tandem mass spectrometry (MS/MS) analysis can provide a further methodology in practicing the methods of the inventon.

[0084] A chemiluminescence assay using a chemiluminescent antibody can be used for sensitive, non-radioactive detection of protein levels. An antibody labeled with fluorochrome also can be suitable. Examples of fluorochromes include, without limitation, DAPI, fluorescein, Hoechst 33258, R-phycocyanin, B-phycoerythrin, R-phycoerythrin, rhodamine, Texas red, and lissamine. Indirect labels include various enzymes well known in the art, such as horseradish peroxidase (HRP), alkaline phosphatase (AP), beta-galactosidase, urease, and the like. Detection systems using suitable substrates for horseradish-peroxidase, alkaline phosphatase , beta-galactosidase are well known in the art.

[0085] A signal from the direct or indirect label can be analyzed, for example, using a spectrophotometer to detect color from a chromogenic substrate; a radiation counter to detect radiation such as a gamma counter for detection of ¹²⁵I; or a fluorometer to detect fluorescence in the presence of light of a certain wavelength. For detection of enzyme-linked antibodies, a quantitative analysis can be made using a spectrophotometer such as an EMAX Microplate Reader (Molecular Devices; Menlo Park, Calif.) in accordance with the manufacturer's instructions. If desired, assays used to practice the invention can be automated or performed robotically, and the signal from multiple samples can be detected simultaneously.

[0086] In some embodiments, the methods described herein encompass quantification of the biomarkers using mass spectrometry (MS). In further embodiments, the mass spectrometry can be liquid chromatography-mass spectrometry (LC-MS), multiple reaction monitoring (MRM) or selected reaction monitoring (SRM). In additional embodiments, the MRM or SRM can further encompass scheduled MRM or scheduled SRM. [0087] As described above, chromatography can also be used in practicing the methods of the invention. Chromatography encompasses methods for separating chemical substances and generally involves a process in which a mixture of analytes is carried by a moving stream of liquid or gas ("mobile phase") and separated into components as a result of differential distribution of the analytes as they flow around or over a stationary liquid or solid phase ("stationary phase"), between the mobile phase and said stationary phase. The stationary phase can be usually a finely divided solid, a sheet of filter material, or a thin film of a liquid on the surface of a solid, or the like. Chromatography is well understood by those skilled in the art as a technique applicable for the separation of chemical compounds of biological origin, such as, e.g., amino acids, proteins, fragments of proteins or peptides, etc.

[0088] Chromatography can be columnar (i.e., wherein the stationary phase is deposited or packed in a column), preferably liquid chromatography, and yet more preferably high- performance liquid chromatography (HPLC) or ultra high performance/pressure liquid chromatography (UHPLC). Particulars of chromatography are well known in the art (Bidlingmeyer, Practical HPLC Methodology and Applications, John Wiley & Sons Inc., 1993). Exemplary types of chromatography include, without limitation, high-performance liquid chromatography (HPLC), UHPLC, normal phase HPLC (NP-HPLC), reversed phase HPLC (RP-HPLC), ion exchange chromatography (IEC), such as cation or anion exchange chromatography, hydrophilic interaction chromatography (HILIC), hydrophobic interaction chromatography (HIC), size exclusion chromatography (SEC) including gel filtration chromatography or gel permeation chromatography, chromatofocusing, affinity

chromatography such as immuno-affmity, immobilised metal affinity chromatography, and the like. Chromatography, including single-, two- or more-dimensional chromatography, can be used as a peptide fractionation method in conjunction with a further peptide analysis method, such as for example, with a downstream mass spectrometry analysis as described elsewhere in this specification.

[0089] Further peptide or polypeptide separation, identification or quantification methods can be used, optionally in conjunction with any of the above described analysis methods, for measuring biomarkers in the present disclosure. Such methods include, without limitation, chemical extraction partitioning, isoelectric focusing (IEF) including capillary isoelectric focusing (CIEF), capillary isotachophoresis (CITP), capillary electrochromatography (CEC), and the like, one-dimensional polyacrylamide gel electrophoresis (PAGE), two-dimensional polyacrylamide gel electrophoresis (2D-PAGE), capillary gel electrophoresis (CGE), capillary zone electrophoresis (CZE), micellar electrokinetic chromatography (ME C), free flow electrophoresis (FFE), etc.

[0090] In the context of the invention, the term "capture agent" refers to a compound that can specifically bind to a target, in particular a biomarker. The term includes antibodies, antibody fragments, nucleic acid-based protein binding reagents (e.g. aptamers, Slow Off-rate Modified Aptamers (SOMAmer™)), protein-capture agents, natural ligands (i.e. a hormone for its receptor or vice versa), small molecules or variants thereof.

[0091] Capture agents can be configured to specifically bind to a target, in particular a biomarker. Capture agents can include but are not limited to organic molecules, such as polypeptides, polynucleotides and other non polymeric molecules that are identifiable to a skilled person. In the embodiments disclosed herein, capture agents include any agent that can be used to detect, purify, isolate, or enrich a target, in particular a biomarker. Any art- known affinity capture technologies can be used to selectively isolate and enrich/concentrate biomarkers that are components of complex mixtures of biological media for use in the disclosed methods.

[0092] Antibody capture agents that specifically bind to a biomarker can be prepared using any suitable methods known in the art. See, e.g., Coligan, Current Protocols in

Immunology (1991); Harlow & Lane, Antibodies: A Laboratory Manual (1988); Goding, Monoclonal Antibodies: Principles and Practice (2d ed. 1986). Antibody capture agents can be any immunoglobulin or derivative therof, whether natural or wholly or partially synthetically produced. All derivatives thereof which maintain specific binding ability are also included in the term. Antibody capture agents have a binding domain that is homologous or largely homologous to an immunoglobulin binding domain and can be derived from natural sources, or partly or wholly synthetically produced. Antibody capture agents can be monoclonal or polyclonal antibodies. In some embodiments, an antibody is a single chain antibody. Those of ordinary skill in the art will appreciate that antibodies can be provided in any of a variety of forms including, for example, humanized, partially humanized, chimeric, chimeric humanized, etc. Antibody capture agents can be antibody fragments including, but not limited to, Fab, Fab', F(ab')2, scFv, Fv, dsFv diabody, and Fd fragments. An antibody capture agent can be produced by any means. For example, an antibody capture agent can be enzymatically or chemically produced by fragmentation of an intact antibody and/or it can be recombinantly produced from a gene encoding the partial antibody sequence. An antibody capture agent can comprise a single chain antibody fragment. Alternatively or additionally, antibody capture agent can comprise multiple chains which are linked together, for example, by disulfide linkages; and, any functional fragments obtained from such molecules, wherein such fragments retain specific-binding properties of the parent antibody molecule. Because of their smaller size as functional components of the whole molecule, antibody fragments can offer advantages over intact antibodies for use in certain immunochemical techniques and experimental applications.

[0093] Suitable capture agents useful for practicing the invention also include aptamers. Aptamers are oligonucleotide sequences that can bind to their targets specifically via unique three dimensional (3-D) structures. An aptamer can include any suitable number of nucleotides and different aptamers can have either the same or different numbers of nucleotides. Aptamers can be DNA or RNA or chemically modified nucleic acids and can be single stranded, double stranded, or contain double stranded regions, and can include higher ordered structures. An aptamer can also be a photoaptamer, where a photoreactive or chemically reactive functional group is included in the aptamer to allow it to be covalently linked to its corresponding target. Use of an aptamer capture agent can include the use of two or more aptamers that specifically bind the same biomarker. An aptamer can include a tag. An aptamer can be identified using any known method, including the SELEX

(systematic evolution of ligands by exponential enrichment), process. Once identified, an aptamer can be prepared or synthesized in accordance with any known method, including chemical synthetic methods and enzymatic synthetic methods and used in a variety of applications for biomarker detection. Liu et al., Curr Med Chem. 18(27):4117-25 (2011). Capture agents useful in practicing the methods of the invention also include SOMAmers (Slow Off-Rate Modified Aptamers) known in the art to have improved off-rate

characteristics. Brody et al, J Mol Biol. 422(5):595-606 (2012). SOMAmers can be generated using using any known method, including the SELEX method.

[0094] It is understood by those skilled in the art that biomarkers can be modified prior to analysis to improve their resolution or to determine their identity. For example, the biomarkers can be subject to proteolytic digestion before analysis. Any protease can be used. Proteases, such as trypsin, that are likely to cleave the biomarkers into a discrete number of fragments are particularly useful. The fragments that result from digestion function as a fingerprint for the biomarkers, thereby enabling their detection indirectly. This is particularly useful where there are biomarkers with similar molecular masses that might be confused for the biomarker in question. Also, proteolytic fragmentation is useful for high molecular weight biomarkers because smaller biomarkers are more easily resolved by mass spectrometry. In another example, biomarkers can be modified to improve detection resolution. For instance, neuraminidase can be used to remove terminal sialic acid residues from glycoproteins to improve binding to an anionic adsorbent and to improve detection resolution. In another example, the biomarkers can be modified by the attachment of a tag of particular molecular weight that specifically binds to molecular biomarkers, further distinguishing them.

Optionally, after detecting such modified biomarkers, the identity of the biomarkers can be further determined by matching the physical and chemical characteristics of the modified biomarkers in a protein database (e.g., SwissProt).

[0095] It is further appreciated in the art that biomarkers in a sample can be captured on a substrate for detection. Traditional substrates include antibody-coated 96-well plates or nitrocellulose membranes that are subsequently probed for the presence of the proteins.

Alternatively, protein-binding molecules attached to microspheres, microparticles, microbeads, beads, or other particles can be used for capture and detection of biomarkers. The protein-binding molecules can be antibodies, peptides, peptoids, aptamers, small molecule ligands or other protein-binding capture agents attached to the surface of particles. Each protein-binding molecule can include unique detectable label that is coded such that it can be distinguished from other detectable labels attached to other protein-binding molecules to allow detection of biomarkers in multiplex assays. Examples include, but are not limited to, color-coded microspheres with known fluorescent light intensities (see e.g., microspheres with xMAP technology produced by Luminex (Austin, Tex.); microspheres containing quantum dot nanocrystals, for example, having different ratios and combinations of quantum dot colors (e.g., Qdot nanocrystals produced by Life Technologies (Carlsbad, Calif); glass coated metal nanoparticles (see e.g., SERS nanotags produced by Nanoplex Technologies, Inc. (Mountain View, Calif); barcode materials (see e.g., sub-micron sized striped metallic rods such as Nanobarcodes produced by Nanoplex Technologies, Inc.), encoded

microparticles with colored bar codes (see e.g., CellCard produced by Vitra Bioscience, vitrabio.com), glass microparticles with digital holographic code images (see e.g., CyVera microbeads produced by Illumina (San Diego, Calif); chemiluminescent dyes, combinations of dye compounds; and beads of detectably different sizes. [0096] In another aspect, biochips can be used for capture and detection of the biomarkers of the invention. Many protein biochips are known in the art. These include, for example, protein biochips produced by Packard Bioscience Company (Meriden Conn.), Zyomyx (Hayward, Calif.) and Phylos (Lexington, Mass.). In general, protein biochips comprise a substrate having a surface. A capture reagent or adsorbent is attached to the surface of the substrate. Frequently, the surface comprises a plurality of addressable locations, each of which location has the capture agent bound there. The capture agent can be a biological molecule, such as a polypeptide or a nucleic acid, which captures other biomarkers in a specific manner. Alternatively, the capture agent can be a chromatographic material, such as an anion exchange material or a hydrophilic material. Examples of protein biochips are well known in the art.

[0097] Measuring mRNA in a biological sample can be used as a surrogate for detection of the level of the corresponding protein biomarker in a biological sample. Thus, any of the biomarkers or biomarker panels described herein can also be detected by detecting the appropriate RNA. Levels of mRNA can measured by reverse transcription quantitative polymerase chain reaction (RT-PCR followed with qPCR). RT-PCR is used to create a cDNA from the mRNA. The cDNA can be used in a qPCR assay to produce fluorescence as the DNA amplification process progresses. By comparison to a standard curve, qPCR can produce an absolute measurement such as number of copies of mRNA per cell. Northern blots, microarrays, Invader assays, and RT-PCR combined with capillary electrophoresis have all been used to measure expression levels of mRNA in a sample. See Gene Expression Profiling: Methods and Protocols. Richard A. Shimkets, editor, Humana Press, 2004.

[0098] Some embodiments disclosed herein relate to diagnostic and prognostic methods of determining the probability for preeclampsia in a pregnant female. The detection of the level of expression of one or more biomarkers and/or the determination of a ratio of biomarkers can be used to determine the probability for preeclampsia in a pregnant female. Such detection methods can be used, for example, for early diagnosis of the condition, to determine whether a subject is predisposed to preeclampsia, to monitor the progress of preeclampsia or the progress of treatment protocols, to assess the severity of preeclampsia, to forecast the outcome of preeclampsia and/or prospects of recovery or birth at full term, or to aid in the determination of a suitable treatment for preeclampsia. [0099] The quantitation of biomarkers in a biological sample can be determined, without limitation, by the methods described above as well as any other method known in the art. The quantitative data thus obtained is then subjected to an analytic classification process. In such a process, the raw data is manipulated according to an algorithm, where the algorithm has been pre-defined by a training set of data, for example as described in the examples provided herein. An algorithm can utilize the training set of data provided herein, or can utilize the guidelines provided herein to generate an algorithm with a different set of data.

[00100] In some embodiments, analyzing a measurable feature to determine the probability for preeclampsia in a pregnant female encompasses the use of a predictive model. In further embodiments, analyzing a measurable feature to determine the probability for preeclampsia in a pregnant female encompasses comparing said measurable feature with a reference feature. As those skilled in the art can appreciate, such comparison can be a direct comparison to the reference feature or an indirect comparison where the reference feature has been incorporated into the predictive model. In further embodiments, analyzing a measurable feature to determine the probability for preeclampsia in a pregnant female encompasses one or more of a linear discriminant analysis model, a support vector machine classification algorithm, a recursive feature elimination model, a prediction analysis of microarray model, a logistic regression model, a CART algorithm, a flex tree algorithm, a LART algorithm, a random forest algorithm, a MART algorithm, a machine learning algorithm, a penalized regression method, or a combination thereof. In particular embodiments, the analysis comprises logistic regression.

[00101] An analytic classification process can use any one of a variety of statistical analytic methods to manipulate the quantitative data and provide for classification of the sample. Examples of useful methods include linear discriminant analysis, recursive feature elimination, a prediction analysis of microarray, a logistic regression, a CART algorithm, a FlexTree algorithm, a LART algorithm, a random forest algorithm, a MART algorithm, machine learning algorithms; etc.

[00102] Classification can be made according to predictive modeling methods that set a threshold for determining the probability that a sample belongs to a given class. The probability preferably is at least 50%, or at least 60%, or at least 70%, or at least 80% or higher. Classifications also can be made by determining whether a comparison between an obtained dataset and a reference dataset yields a statistically significant difference. If so, then the sample from which the dataset was obtained is classified as not belonging to the reference dataset class. Conversely, if such a comparison is not statistically significantly different from the reference dataset, then the sample from which the dataset was obtained is classified as belonging to the reference dataset class.

[00103] The predictive ability of a model can be evaluated according to its ability to provide a quality metric, e.g. AUC (area under the curve) or accuracy, of a particular value, or range of values. Area under the curve measures are useful for comparing the accuracy of a classifier across the complete data range. Classifiers with a greater AUC have a greater capacity to classify unknowns correctly between two groups of interest. In some

embodiments, a desired quality threshold is a predictive model that will classify a sample with an accuracy of at least about 0.7, at least about 0.75, at least about 0.8, at least about 0.85, at least about 0.9, at least about 0.95, or higher. As an alternative measure, a desired quality threshold can refer to a predictive model that will classify a sample with an AUC of at least about 0.7, at least about 0.75, at least about 0.8, at least about 0.85, at least about 0.9, or higher.

[00104] As is known in the art, the relative sensitivity and specificity of a predictive model can be adjusted to favor either the selectivity metric or the sensitivity metric, where the two metrics have an inverse relationship. The limits in a model as described above can be adjusted to provide a selected sensitivity or specificity level, depending on the particular requirements of the test being performed. One or both of sensitivity and specificity can be at least about 0.7, at least about 0.75, at least about 0.8, at least about 0.85, at least about 0.9, or higher.

[00105] The raw data can be initially analyzed by measuring the values for each biomarker, usually in triplicate or in multiple triplicates. The data can be manipulated, for example, raw data can be transformed using standard curves, and the average of triplicate measurements used to calculate the average and standard deviation for each patient. These values can be transformed before being used in the models, e.g. log-transformed, Box-Cox transformed (Box and Cox, Royal Stat. Soc, Series B, 26:211-246(1964). The data are then input into a predictive model, which will classify the sample according to the state. The resulting information can be communicated to a patient or health care provider. [00106] To generate a predictive model for preeclampsia, a robust data set, comprising known control samples and samples corresponding to the preeclampsia classification of interest is used in a training set. A sample size can be selected using generally accepted criteria. As discussed above, different statistical methods can be used to obtain a highly accurate predictive model. Examples of such analysis are provided in Example 2.

[00107] In one embodiment, hierarchical clustering is performed in the derivation of a predictive model, where the Pearson correlation is employed as the clustering metric. One approach is to consider a preeclampsia dataset as a "learning sample" in a problem of "supervised learning." CART is a standard in applications to medicine (Singer , Recursive Partitioning in the Health Sciences. Springer(1999)) and can be modified by transforming any qualitative features to quantitative features; sorting them by attained significance levels, evaluated by sample reuse methods for Hotelling's T² statistic; and suitable application of the lasso method. Problems in prediction are turned into problems in regression without losing sight of prediction, indeed by making suitable use of the Gini criterion for classification in evaluating the quality of regressions.

[00108] This approach led to what is termed FlexTree (Huang, Proc. Nat. Acad. Sci. U.S.A 101 : 10529-10534(2004)). FlexTree performs very well in simulations and when applied to multiple forms of data and is useful for practicing the claimed methods. Software automating FlexTree has been developed. Alternatively, LARTree or LART can be used (Turnbull (2005) Classification Trees with Subset Analysis Selection by the Lasso, Stanford University). The name reflects binary trees, as in CART and FlexTree; the lasso, as has been noted; and the implementation of the lasso through what is termed LARS by Efron et al. (2004) Annals of Statistics 32:407-4 1 (2004). See, also, Huang et al.., Proc. Natl. Acad. Sci. USA. 101(29): 10529-34 (2004). Other methods of analysis that can be used include logic regression. One method of logic regression Ruczinski , Journal of Computational and Graphical Statistics 12:475-512 (2003). Logic regression resembles CART in that its classifier can be displayed as a binary tree. It is different in that each node has Boolean statements about features that are more general than the simple "and" statements produced by CART.

[00109] Another approach is that of nearest shrunken centroids (Tibshirani, Proc. Natl. Acad. Sci. U.S.A 99:6567-72(2002)). The technology is k-means-like, but has the advantage that by shrinking cluster centers, one automatically selects features, as is the case in the lasso, to focus attention on small numbers of those that are informative. The approach is available as PAM software and is widely used. Two further sets of algorithms that can be used are random forests (Breiman, Machine Learning 45:5-32 (2001)) and MART (Hastie, The Elements of Statistical Learning, Springer (2001)). These two methods are known in the art as "committee methods," that involve predictors that "vote" on outcome.

[00110] To provide significance ordering, the false discovery rate (FDR) can be determined. First, a set of null distributions of dissimilarity values is generated. In one embodiment, the values of observed profiles are permuted to create a sequence of distributions of correlation coefficients obtained out of chance, thereby creating an appropriate set of null distributions of correlation coefficients (Tusher et al. , Proc. Natl. Acad. Sci. U.S.A 98, 5116-21 (2001)). The set of null distribution is obtained by: permuting the values of each profile for all available profiles; calculating the pair-wise correlation coefficients for all profile; calculating the probability density function of the correlation coefficients for this permutation; and repeating the procedure for N times, where N is a large number, usually 300. Using the N distributions, one calculates an appropriate measure (mean, median, etc.) of the count of correlation coefficient values that their values exceed the value (of similarity) that is obtained from the distribution of experimentally observed similarity values at given significance level.

[00111] The FDR is the ratio of the number of the expected falsely significant correlations (estimated from the correlations greater than this selected Pearson correlation in the set of randomized data) to the number of correlations greater than this selected Pearson correlation in the empirical data (significant correlations). This cut-off correlation value can be applied to the correlations between experimental profiles. Using the aforementioned distribution, a level of confidence is chosen for significance. This is used to determine the lowest value of the correlation coefficient that exceeds the result that would have obtained by chance. Using this method, one obtains thresholds for positive correlation, negative correlation or both. Using this threshold(s), the user can filter the observed values of the pair wise correlation coefficients and eliminate those that do not exceed the threshold(s). Furthermore, an estimate of the false positive rate can be obtained for a given threshold. For each of the individual "random correlation" distributions, one can find how many observations fall outside the threshold range. This procedure provides a sequence of counts. The mean and the standard deviation of the sequence provide the average number of potential false positives and its standard deviation.

[00112] In an alternative analytical approach, variables chosen in the cross-sectional analysis are separately employed as predictors in a time-to-event analysis (survival analysis), where the event is the occurrence of preeclampsia, and subjects with no event are considered censored at the time of giving birth. Given the specific pregnancy outcome (preeclampsia event or no event), the random lengths of time each patient will be observed, and selection of proteomic and other features, a parametric approach to analyzing survival can be better than the widely applied semi-parametric Cox model. A Weibull parametric fit of survival permits the hazard rate to be monotonically increasing, decreasing, or constant, and also has a proportional hazards representation (as does the Cox model) and an accelerated failure-time representation. All the standard tools available in obtaining approximate maximum likelihood estimators of regression coefficients and corresponding functions are available with this model.

[00113] In addition the Cox models can be used, especially since reductions of numbers of covariates to manageable size with the lasso will significantly simplify the analysis, allowing the possibility of a nonparametric or semi-parametric approach to prediction of time to preeclampsia. These statistical tools are known in the art and applicable to all manner of proteomic data. A set of biomarker, clinical and genetic data that can be easily determined, and that is highly informative regarding the probability for preeclampsia and predicted time to a preeclampsia event in said pregnant female is provided. Also, algorithms provide information regarding the probability for preeclampsia in the pregnant female.

[00114] In the development of a predictive model, it can be desirable to select a subset of markers, i.e. at least 3, at least 4, at least 5, at least 6, up to the complete set of markers. Usually a subset of markers will be chosen that provides for the needs of the quantitative sample analysis, e.g. availability of reagents, convenience of quantitation, etc., while maintaining a highly accurate predictive model. The selection of a number of informative markers for building classification models requires the definition of a performance metric and a user-defined threshold for producing a model with useful predictive ability based on this metric. For example, the performance metric can be the AUROC, the sensitivity and/or specificity of the prediction as well as the overall accuracy of the prediction model. [00115] As will be understood by those skilled in the art, an analytic classification process can use any one of a variety of statistical analytic methods to manipulate the quantitative data and provide for classification of the sample. Examples of useful methods include, without limitation, linear discriminant analysis, recursive feature elimination, a prediction analysis of microarray, a logistic regression, a CART algorithm, a FlexTree algorithm, a LART algorithm, a random forest algorithm, a MART algorithm, and machine learning algorithms.

[00116] As described in Example 2, various methods are used in a training model. The selection of a subset of markers can be for a forward selection or a backward selection of a marker subset. The number of markers can be selected that will optimize the performance of a model without the use of all the markers. One way to define the optimum number of terms is to choose the number of terms that produce a model with desired predictive ability (e.g. an AUC>0.75, or equivalent measures of sensitivity/specificity) that lies no more than one standard error from the maximum value obtained for this metric using any combination and number of terms used for the given algorithm.

[00117] Table 1. Transitions with p-values less than 0.05 in univariate Cox Proportional Hazards to predict Gestational Age of time to event (preeclampsia).

YWGVASFLQK 599.82 350.2 0.00 RET4 HUMAN

VSALLTPAEQTGTWK 801.43 371.2 0.00 APOB HUMAN

DPNGLPPEAQK 83.3 497.2 0.00 RET4 HUMAN

VNHVTLSQP _561.82_673.4 0.00 B2MG HUMAN

DALSSVQESQVAQQAPv_572.96_502.3 0.00 APOC3 HUMAN

IAQYYYTFK_598.8_884.4 0.00 F13B_HUMAN

IEEIAAK 387.22 531.3 0.00 C05_HUMAN

GWVTDGFSSLK_598.8_854.4 0.00 APOC3 HUMAN

VNHVTLSQP _561.82_351.2 0.00 B2MG HUMAN

ITENDIQIALDDAK_779.9_873.5 0.00 APOB HUMAN

VSALLTPAEQTGTWK 801.43 585.4 0.00 APOB HUMAN

VILGAHQEVNLEPHVQEIEVSR 832.78

0.00 PLMN HUMAN 860.4

SPELQAEAK_486.75_788.4 0.00 APOA2 HUMAN

SPELQAEAK 486.75 659.4 0.00 APOA2 HUMAN

DYWSTVK_449.72_620.3 0.00 APOC3 HUMAN

VPLALFALNR 557.34_620.4 0.00 PEPD HUMAN

TSDQIHFFFAK 447.56 659.4 0.00 ANT3_HUMAN

DALSSVQESQVAQQAR 572.96 672.4 0.00 APOC3 HUMAN

VIAVNEVGR_478.78_284.2 0.00 CHL1 HUMAN

LLEVPEGR 456.76 686.3 0.00 C1S HUMAN

VEPLYELVTATDFAYSSTVR 754.38 5

0.00 C08B_HUMAN 49.3

HHGPTITAK 321.18 275.1 0.01 AMBP HUMAN

ALNFGGIGVVVGHELTHAFDDQGR 8

0.01 ECE1 HUMAN 37.09_299.2

ETLLQDFR 511.27_565.3 0.01 AMBP HUMAN

HHGPTIT AK 321.18 432.3 0.01 AMBP HUMAN

IIGGSDADI _494.77_260.2 0.01 C1S HUMAN [00118] Table 2. Top 40 transitions with p-values less than 0.05 in univariate Cox Proportional Hazards to predict Gestational Age of time to event (preeclampsia), sorted by protein ID.

cox

Transition pvalues protein

VNHVTLSQPK 561.82 673.4 0.00 B2MG HUMAN

VNHVTLSQPK 561.82 351.2 0.00 B2MG HUMAN

SSNNPHSPIVEEFQVPYNK_729.36_261.2 0.00 C1S_HUMAN

IIGGSDADIK 494.77 762.4 0.00 C1S_HUMAN

LLEVPEGR 456.76 686.3 0.00 C1S_HUMAN

IIGGSDADIK_494.77_260.2 0.01 C1S_HUMAN

VIAV EVGR_478.78_284.2 0.00 CHL1_HUMAN

IEEIAAK_387.22_531.3 0.00 C05 HUMAN

VEPLYELVTATDFAYSSTVR 754.38 549.3 0.00 CO 8B HUMAN

ALNFGGIGVVVGHELTHAFDDQGR 837.09 299.2 0.01 ECE1 HUMAN

IAQYYYTF _598.8_884.4 0.00 F13B HUMAN

TLLIANETLR_572.34_703.4 0.00 IL5 HUMAN

VPLALFALNR_557.34_620.4 0.00 PEPD HUMAN

VILGAHQEVNLEPHVQEIEVSR 832.78 860.4 0.00 PLMN HUMAN

DPNGLPPEAQK 83.3 669.4 0.00 RET4 HUMAN

YWGVASFLQK_599.82_849.5 0.00 RET4 HUMAN

YWGVASFLQK 599.82 350.2 0.00 RET4 HUMAN

DPNGLPPEAQK 583.3 497.2 0.00 RET4 HUMAN

[00119] Table 3. Transitions selected by Cox stepwise AIC analysis

3.21E+0

SPEQQETVLDGNLIIR_906.48_685.4 17.28 7 7.49 2.31 0.02

EPGLCTWQSLR_673.83_790.4 -2.08 1.25E-01 1.02 -2.05 0.04

[00120] Table 4. Transitions selected by Cox lasso analysis

[00121] Table 5. Area under the ROC curve for individual analytes to discriminate preeclampsia subjects from non-preeclampsia subjects. The 196 transitions with the highest ROC area are shown.

Transition ROC area

FSVVYAK 407.23 381.2 0.80

LLEVPEGR 456.76 686.3 0.80

SPELQAEAK_486.75_659.4 0.80

VVLSSGSGPGLDLPLVLGLPLQLK 791.48 598.4 0.79

ETLLQDFR 511.27 565.3 0.79

VNHVTLSQP _561.82_351.2 0.79

VVGGLVALR_442.29_685.4 0.79

YTTEIIK 434.25 603.4 0.79

DPNGLPPEAQK_583.3_669.4 0.78

EDTPNSVWEPAK 686.82 315.2 0.78

GWVTDGFSSLK 598.8 854.4 0.78

HHGPTIT AK 321.18_432.3 0.78

LHEAFSPVSYQHDLALLR 699.37 251.2 0.78

GA.of.Time.to.Event.in.Days 0.77

DALSSVQESQVAQQAR_572.96_502.3 0.77

DYWSTVK_449.72_347.2 0.77

IAQYYYTFK_598.8_395.2 0.77

YWGVASFLQK_599.82_849.5 0.77

AHYDLR 387.7 288.2 0.76

EDTPNS V WEP AK 686.82 630.3 0.76

GDTYPAELYITGSILR_884.96_922.5 0.76

SVSLPSLDPASAK_636.35_885.5 0.76

TSESGELHGLTTEEEFVEGIYK 819.06 310.2 0.76

ALEQDLPVNI _620.35_570.4 0.75

HHGPTIT AK 321.18 275.1 0.75

IAQYYYTFK_598.8_884.4 0.75

ITENDIQIALDDAK_779.9_632.3 0.75

LPNNVLQEK 527.8 844.5 0.75

YWGVASFLQK 599.82 350.2 0.75

FQLPGQK_409.23_276.1 0.75 Transition ROC area

HTLNQIDEVK 598.82 958.5 0.75

VVLSSGSGPGLDLPLVLGLPLQL 791.48 768.5 0.75

DADPDTFFAK_563.76_302.1 0.74

DADPDTFFAK 563.76 825.4 0.74

FQLPGQK 409.23 429.2 0.74

HFQNLGK_422.23_527.2 0.74

VIAV EVGR_478.78_284.2 0.74

VPLALFALNR 557.34_620.4 0.74

ETLLQDFR 11.27_322.2 0.73

FNAVLTNPQGDYDTSTGK 964.46 262.1 0.73

SVSLPSLDPASAK_636.35_473.3 0.73

AHYDLR_387.7_566.3 0.72

ALNHLPLEYNSALYSR 620.99 538.3 0.72

A WV A WR 394.71 258.1 0.72

AWVAWR_394.71_531.3 0.72

ETAASLLQ AGYK 626.33 879.5 0.72

IALGGLLFP ASNLR 481.29 657.4 0.72

IAPQLSTEELVSLGEK_857.47_533.3 0.72

ITENDIQIALDDAK 779.9 873.5 0.72

VAPEEHPVLLTEAPLNPK 652.03 869.5 0.71

EPGLCTWQSLR_673.83_375.2 0.71

IAPQLSTEELVSLGEK 857.47 333.2 0.71

SPEQQETVLDGNLIIR_906.48_699.3 0.71

VSALLTPAEQTGTWK 801.43 371.2 0.71

VSALLTPAEQTGTWK 801.43 585.4 0.71

VSEADSSNADWVTK_754.85_347.2 0.71

GDTYPAELYITGSILR_884.96_274.1 0.70

IPGIFELGISSQSDR 809.93 849.4 0.70

IQTHSTTYR 369.52 540.3 0.70

LLDSLPSDTR_558.8_890.4 0.70 Transition ROC area

QLGLPGPPD VPDHAAYHPF 676.67 299.2 0.70

SYELPDGQVITIGNER 895.95 251.1 0.70

VILGAHQEVNLEPHVQEIEVSR_832.78_860.4 0.70

WGAAPYR 410.71 577.3 0.69

DFHINLFQVLPWLK 885.49 543.3 0.69

LLDSLPSDTR_558.8_276.2 0.69

VEPLYELVTATDFAYSSTVR_754.38_549.3 0.69

VPTADLEDVLPLAEDITNILSK 789.43 841.4 0.69

GGEGTGYFVDFSVR_745.85_869.5 0.69

HTLNQIDEVK 598.82 951.5 0.69

LIENGYFHPVK 439.57_627.4 0.69

LPNNVLQEK_527.8_730.4 0.69

NKPGVYTDVAYYLAWIR 677.02 545.3 0.69

NTVISV PSTK_580.32_845.5 0.69

QLGLPGPPD VPDHAAYHPF_676.67_263.1 0.69

YTTEIIK 434.25 704.4 0.69

LPDATPK 371.21 628.3 0.68

IEGNLIFDPNNYLPK_873.96_845.5 0.68

LEQGENVFLQATDK 796.4 822.4 0.68

TLYSSSPR_455.74_533.3 0.68

TLYSSSPR_455.74_696.3 0.68

VSEADSSNADWVTK_754.85_533.3 0.68

DGSPDVTTADIGANTPDAT 973.45 844.4 0.67

EWVAIESDSVQPVPR_856.44_486.2 0.67

IALGGLLFPASNLR 481.29 412.3 0.67

IEEIAAK 387.22 531.3 0.67

IEGNLIFDPNNYLPK_873.96_414.2 0.67

LYYGDDEK 501.72 726.3 0.67

TGISPLALIK 506.82 741.5 0.67

VPTADLEDVLPLAEDITNILSK_789.43_940.5 0.67 Transition ROC area

ADSQAQLLLSTVVGVFTAPGLHL 822.46 983.6 0.66

AYSDLSR_406.2_577.3 0.66

DFHINLFQVLPWLK_885.49_400.2 0.66

DLHLSDVFLK 396.22 260.2 0.66

EWVAIESDSVQPVPR 856.44 468.3 0.66

FNAVLTNPQGDYDTSTGK_964.46_333.2 0.66

LSSPAVITDK_515.79_743.4 0.66

LYYGDDEK 501.72 563.2 0.66

SGFSFGFK_438.72_732.4 0.66

IIEVEEEQEDPYLNDR 995.97 777.4 0.66

AVYEAVLR 460.76 750.4 0.66

WGAAPYR_410.71_634.3 0.66

FTFTLHLETPKPSISSSNLNPR 829.44 874.4 0.65

DAQYAPGYDK 564.25 315.1 0.65

YGLVTYATYPK_638.33_334.2 0.65

DGSPDVTTADIGANTPDATK 973.45 531.3 0.65

ETAASLLQAGYK 626.33 679.4 0.65

ALNHLPLEYNSALYSR_620.99_696.4 0.65

DISEVVTPR_508.27_787.4 0.65

IS.2_662.3_313.1 0.65

IVLGQEQDSYGGK_697.35_261.2 0.65

IVLGQEQDSYGGK 697.35 754.3 0.65

TLEAQLTPR 514.79 685.4 0.65

VPVAVQGEDTVQSLTQGDGVAK_733.38_775.4 0.65

V APEEHP VLLTE APLNPK 652.03 568.3 0.64

ADSQAQLLLSTVVGVFTAPGLHLK 822.46 664.4 0.64

AEAQAQYSAAVAK_654.33_908.5 0.64

DISEVVTPR_508.27_472.3 0.64

ELLESYIDGR 597.8 710.3 0.64

TGISPLALIK 506.82 654.5 0.64 Transition ROC area

TNLESILS YPK_632.84_807.5 0.64

DAQYAPGYDK 564.25 813.4 0.63

LPTAWPLR_483.31_755.5 0.63

DSPVLIDFFEDTER 841.9 512.3 0.63

FAFNLYR 465.75_712.4 0.63

FVFGTTPEDILR 697.87_843.5 0.63

GDSGGAFAVQDPNDK 739.33 473.2 0.63

SLDFTELDVAAEK 719.36 316.2 0.63

SLLQPNK 400.24 99.4 0.63

TLLIANETLR 572.34 816.5 0.63

VILGAHQEVNLEPHVQEIEVSR 832.78 603.3 0.63

VQEAHLTEDQIFYFPK_655.66_701.4 0.63

FTFTLHLETPKPSISSSNLNPR 829.44 787.4 0.63

AYSDLSR_406.2_375.2 0.62

DDLYVSDAFHK 655.31 344.1 0.62

DDLYVSDAFHK 65 .31 704.3 0.62

DPDQTDGLGLSYLSSHIANVER 796.39 456.2 0.62

ESDTSYVSLK_564.77_347.2 0.62

ESDTSYVSLK 564.77 696.4 0.62

FVFGTTPEDILR 697.87 742.4 0.62

ILDDLSPR_464.76_587.3 0.62

LEQGENVFLQATDK 796.4 675.4 0.62

LHEAFSPVSYQHDLALLR 699.37 380.2 0.62

LIENGYFHPVK_439.57_343.2 0.62

SLPVSDSVLSGFEQR 810.92 836.4 0.62

T WDPEGVIFYGDTNPK 919.93 403.2 0.62

VGEYSLYIGR_578.8_708.4 0.62

VIAVNEVGR_478.78_744.4 0.62

VPGTSTSATLTGLTR 731.4 761.5 0.62

YEVQGEVFTKPQLWP_910.96_293.1 0.62 Transition ROC area

AFTECCVVASQLR_770.87_673.4 0.61

APLTKPLK 289.86 357.3 0.61

DSPVLIDFFEDTER 841.9 399.2 0.61

ELLESYIDGR 597.8 839.4 0.61

FLQEQGHR 338.84 369.2 0.61

IQTHSTTYR 369.52 627.3 0.61

IS.3_432.6_397.3 0.61

IS.4_706.3_780.3 0.61

IS.4_706.3_927.4 0.61

IS.5 726.3 876.3 0.61

ISLLLIESWLEPVR 834.49 500.3 0.61

LQGTLPVE AR 542.31_842.5 0.61

NiCPGVYTDVAYYLAWIR 677.02 821.5 0.61

SLDFTELD VAAEK 719.36 874.5 0.61

SYTITGLQPGTDYK_772.39_352.2 0.61

TASDFITK 441.73 710.4 0.61

VLSALQAVQGLLVAQGR 862.02 941.6 0.61

VTGWGNLK_437.74_617.3 0.61

YEVQGEVFTKPQLWP 910.96 392.2 0.61

AFIQLWAFDAVK 704.89 650.4 0.60

APLTKPL _289.86_260.2 0.60

GYVIIKPLVWV 643.9 304.2 0.60

IITGLLEFEVYLEYLQNR 738.4 822.4 0.60

ILDDLSPR_464.76_702.3 0.60

LSSPAVITDK 515.79 830.5 0.60

TDAPDLPEENQAR 728.34 843.4 0.60

TFTLLDPK_467.77_359.2 0.60

TFTLLDPK 467.77 686.4 0.60

VLEPTLK 400.25 587.3 0.60

YEFLNGR_449.72_606.3 0.60 Transition ROC area

YGLVTYATYPK 638.33 843.4 0.60

[00122] Table 6. AUROCs for random forest, boosting, lasso, and logistic regression models for a specific number of transitions permitted in the model, as estimated by 100 rounds of bootstrap resampling.

[00123] Table 7. Top 15 transitions selected by each multivariate method, ranked by importance for that method.

rf boosting lasso logit

SS PHSPIVE TSESGELHGLTT

EFQVPYNK 72 DALSSVQESQVAQ EEEFVEGIYK 81 AFTECCVVASQLR 7

4 9.36 261.2 QAR 572.96 502.3 9.06 310.2 70.87 574.3

SSNNPHSPIVE SSNNPHSPIVEE ADSQAQLLLSTVVG

EFQVPYNK 72 AHYDLR 387.7 28 FQVPYNK 729.3 VFTAPGLHLK 822.46

5 9.36 521.3 8.2 6 261.2 664.4

VVLSSGSGPGL

VVGGLVALR FQLPGQK 409.23 DLPLVLGLPLQL AEAQAQYSAAVAK

6 442.29 784.5 276.1 K 791.48 598.4 654.33 908.5

ADSQAQLLLSTVVG

FQLPGQK 409. AFTECCVVASQLR ALEQDLPVNIK VFTAPGLHLK 822.46

7 23 276.1 770.87 673.4 620.35 570.4 983.6

TLLIANETLR ALNHLPLEYNSAL IQTHSTTYR 369 AFTECCVVASQLR 7

8 572.34 703.4 YSR 620.99 538.3 .52 540.3 70.87 673.4

ADSQAQLLLSTVV SSNNPHSPIVEE

DYWSTVK 44 GVFTAPGLHLK 82 FQVPYNK 729.3 Collection. Window . GA.

9 9.72 620.3 2.46 664.4 6 521.3 in.Days

VVGGLVALR AEAQAQYSAAVA FSVVYAK 407.2

10 442.29 685.4 K 654.33 908.5 3 579.4 AHYDLR 387.7 288.2

ADSQAQLLLSTVV

DPNGLPPEAQ GVFTAPGLHLK 82 IAQYYYTFK 59

11 K 583.3 497.2 2.46 983.6 8.8 884.4 AHYDLR 387.7 566.3

AITPPHP AS Q ANIIF

LLEVPEGR 45 DITEGNLR 825.77 IAQYYYTFK 59 AITPPHPASQANIIFDI

12 6.76 356.2 459.3 8.8 395.2 TEGNLR 825.77 459.3

GDTYPAELYITG

GWVTDGFSSL Collection. Window.G SILR 884.96 922. AITPPHPASQANIIFDI

13 K 598.8 953.5 A.in.Days 5 TEGNLR 825.77 917.5

VILGAHQEVN SPEQQETVLDG LEPHVQEIEVS AEAQAQYSAAVA NLIIR 906.48 69 ALEQDLPVNIK 620.3

14 R 832.78 860.4 K 654.33 709.4 9.3 5 570.4

IAPQLSTEELVS

FQLPGQK 409. AFIQLWAFDAVK LGEK 857.47 53 ALEQDLPVNIK 620.3

15 23 429.2 704.89 650.4 3.3 5 798.5

[00124] In yet another aspect, the invention provides kits for determining probability of preeclampsia, wherein the kits can be used to detect N of the isolated biomarkers listed in Tables 2, 3, 4, 5 and 7 through 22. For example, the kits can be used to detect one or more, two or more, three or more, four or more, or five of the isolated biomarkers selected from the group consisting of SPELQAEAK, SSNNPHSPIVEEFQVPYN, VNHVTLSQPK,

VVGGLVALR, and FSVVYAK, LDFHFSSDR, TVQAVLTVPK, GPGEDFR,

ETLLQDFR, ATVVYQGER, and GFQALGDAADIR. In another aspect, the kits can be used to detect one or more, two or more, three or more, four or more, five or more, six or more, seven or more, or eight of the isolated biomarkers selected from the group consisting of alpha- 1 -microglobulin (AMBP), ADP/ATP translocase 3 (ANT3), apolipoprotein A-II (APOA2), apolipoprotein B (APOB), apolipoprotein C-III (APOC3), beta-2-microglobulin (B2MG), complement component 1, s subcomponent (C I S), and retinol binding protein 4 (RBP4 or RET4), Inhibin beta C chain (INHBC), Pigment epithelium-derived factor (PEDF), Prostaglandin-^ D-isomerase (PTGDS), alpha- 1 -microglobulin (AMBP), Beta-2- glycoprotein 1 (APOH), Metalloproteinase inhibitor 1 (TIMP1), Coagulation factor XIII B chain (F13B), Alpha-2-HS-glycoprotein (FETUA), Sex hormone-binding globulin (SHBG).

[00125] The kit can include one or more agents for detection of biomarkers, a container for holding a biological sample isolated from a pregnant female; and printed instructions for reacting agents with the biological sample or a portion of the biological sample to detect the presence or amount of the isolated biomarkers in the biological sample. The agents can be packaged in separate containers. The kit can further comprise one or more control reference samples and reagents for performing an immunoassay.

[00126] In one embodiment, the kit comprises agents for measuring the levels of at least N of the isolated biomarkers listed in Tables 2, 3, 4, 5 and 7 through 22. The kit can include antibodies that specifically bind to these biomarkers, for example, the kit can contain at least one of an antibody that specifically binds to alpha- 1 -microglobulin (AMBP), an antibody that specifically binds to ADP/ATP translocase 3 (ANT3), an antibody that specifically binds to apolipoprotein A-II (APOA2), an antibody that specifically binds to apolipoprotein C-III (APOC3), an antibody that specifically binds to apolipoprotein B (APOB), an antibody that specifically binds to beta-2-microglobulin (B2MG), an antibody that specifically binds to retinol binding protein 4 (RBP4 or RET4), an antibody that specifically binds to Inhibin beta C chain (INHBC), an antibody that specifically binds to Pigment epithelium-derived factor (PEDF), an antibody that specifically binds to Prostaglandin-H2 D-isomerase (PTGDS), an antibody that specifically binds to alpha- 1 -microglobulin (AMBP), an antibody that specifically binds to Beta-2-glycoprotein 1 (APOH), an antibody that specifically binds to Metalloproteinase inhibitor 1 (TIMP1), an antibody that specifically binds to Coagulation factor XIII B chain (F13B), an antibody that specifically binds to Alpha-2-HS-glycoprotein (FETUA), and an antibody that specifically binds to Sex hormone-binding globulin (SHBG).

[00127] The kit can comprise one or more containers for compositions contained in the kit. Compositions can be in liquid form or can be lyophilized. Suitable containers for the compositions include, for example, bottles, vials, syringes, and test tubes. Containers can be formed from a variety of materials, including glass or plastic. The kit can also comprise a package insert containing written instructions for methods of determining probability of preeclampsia.

[00128] From the foregoing description, it will be apparent that variations and

modifications can be made to the invention described herein to adopt it to various usages and conditions. Such embodiments are also within the scope of the following claims.

[00129] The recitation of a listing of elements in any definition of a variable herein includes definitions of that variable as any single element or combination (or

subcombination) of listed elements. The recitation of an embodiment herein includes that embodiment as any single embodiment or in combination with any other embodiments or portions thereof.

[00130] All patents and publications mentioned in this specification are herein

incorporated by reference to the same extent as if each independent patent and publication was specifically and individually indicated to be incorporated by reference.

[00131] The following examples are provided by way of illustration, not limitation.

EXAMPLES

Example 1. Development of Sample Set for Discovery and Validation of Biomarkers for Preeclampsia

[00132] A standard protocol was developed governing conduct of the Proteomic

Assessment of Preterm Risk (PAPR) clinical study. This protocol also provided the option that the samples and clinical information could be used to study other pregnancy

complications. Specimens were obtained from women at 11 Internal Review Board (IRB) approved sites across the United States. After providing informed consent, serum and plasma samples were obtained, as well as pertinent information regarding the patient's demographic characteristics, past medical and pregnancy history, current pregnancy history and concurrent medications. Following delivery, data were collected relating to maternal and infant conditions and complications. Serum and plasma samples were processed according to a protocol that requires standardized refrigerated centrifugation, aliquoting of the samples into 0.5 ml 2-D bar-coded cryovials and subsequent freezing at -80^°C. [00133] Following delivery, preeclampsia cases were individually reviewed. Only preterm preeclampsia cases were used for this analysis. For discovery of biomarkers of preeclampsia, 20 samples collected between 17-28 weeks of gestation were analyzed. Samples included 9 cases, 9 term controls matched within one week of sample collection and 2 random term controls. The samples were processed in batches of 24 that included 20 clinical samples and 4 identical human gold standards (HGS). HGS samples are identical aliquots from a pool of human blood and were used for quality control. HGS samples were placed in position 1, 8, 15 and 24 of a batch with patient samples processed in the remaining 20 positions. Matched cases and controls were always processed adjacently.

[00134] The samples were subsequently depleted of high abundance proteins using the Human 14 Multiple Affinity Removal System (MARS 14), which removes 14 of the most abundant proteins that are essentially uninformative with regard to the identification for disease-relevant changes in the serum proteome. To this end, equal volumes of each clinical or HGS sample were diluted with column buffer and filtered to remove precipitates. Filtered samples were depleted using a MARS-14 column (4.6 x 100 mm, Cat. #5188-6558, Agilent Technologies). Samples were chilled to 4^°C in the autosampler, the depletion column was run at room temperature, and collected fractions were kept at 4^°C until further analysis. The unbound fractions were collected for further analysis.

[00135] A second aliquot of each clinical serum sample and of each HGS was diluted into ammonium bicarbonate buffer and depleted of the 14 high and approximately 60 additional moderately abundant proteins using an IgY14-SuperMix (Sigma) hand-packed column, comprised of 10 mL of bulk material (50% slurry, Sigma). Shi et al , Methods, 56(2):246-53 (2012). Samples were chilled to 4^°C in the autosampler, the depletion column was run at room temperature, and collected fractions were kept at 4^°C until further analysis. The unbound fractions were collected for further analysis.

[00136] Depleted serum samples were denatured with trifluorethanol, reduced with dithiotreitol, alkylated using iodoacetamide, and then digested with trypsin at a 1 : 10 trypsin: protein ratio. Following trypsin digestion, samples were desalted on a CI 8 column, and the eluate lyophilized to dryness. The desalted samples were resolubilized in a reconstitution solution containing five internal standard peptides. [00137] Depleted and trypsin digested samples were analyzed using a scheduled Multiple Reaction Monitoring method (sMRM). The peptides were separated on a 150 mm x 0.32 mm Bio-Basic C 18 column (ThermoFisher) at a flow rate of 5 μΐ/min using a Waters Nano Acquity UPLC and eluted using an acetonitrile gradient into a AB SCIEX QTRAP 5500 with a Turbo V source (AB SCIEX, Framingham, MA). The sMRM assay measured 1708 transitions that correspond to 854 peptides and 236 proteins. Chromatographic peaks were integrated using Rosetta Elucidator software (Ceiba Solutions).

[00138] Transitions were excluded from analysis, if their intensity area counts were less than 10000 and if they were missing in more than three samples per batch. Intensity area counts were log transformed and Mass Spectrometry run order trends and depletion batch effects were minimized using a regression analysis.

Example 2. Analysis of Transitions to Identify PE Biomarkers

[00139] The objective of these analyses was to examine the data collected in Example 1 to identify transitions and proteins that predict preeclampsia. The specific analyses employed were (i) Cox time-to-event analyses and (ii) models with preeclampsia as a binary categorical dependent variable. The dependent variable for all the Cox analyses was Gestational Age of time to event (where event is preeclampsia). For the purpose of the Cox analyses, preeclampsia subjects have the event on the day of birth. Non-preeclampsia subjects are censored on the day of birth. Gestational age on the day of specimen collection is a covariate in all Cox analyses.

[00140] The assay data obtained in Example 1 were previously adjusted for run order and log transformed. The data was not further adjusted. There were 9 matched non-preeclampsia subjects, and two unmatched non-preeclampsia subjects, where matching was done according to center, gestational age and ethnicity.

Univariate Cox Proportional Hazards Analyses

[00141] Univariate Cox Proportional Hazards analyses was performed to predict

Gestational Age of time to event (preeclampsia), including Gestational age on the day of specimen collection as a covariate. Table 1 shows the 40 transitions with p-values less than 0.05. Table 2 shows the same transitions sorted by protein ID. There are 8 proteins that have multiple transitions with p-values less than 0.05: AMBP, ANT3, APOA2, APOB, APOC3, B2MG, CIS, and RET4.

Multivariate Cox Proportional Hazards Analyses: Stepwise AIC selection

[00142] Cox Proportional Hazards analyses was performed to predict Gestational Age of time to event (preeclampsia), including Gestational age on the day of specimen collection as a covariate, using stepwise and lasso models for variable selection. The stepwise variable selection analysis used the Akaike Information Criterion (AIC) as the stopping criterion. Table 3 shows the transitions selected by the stepwise AIC analysis. The coefficient of determination (R²) for the stepwise AIC model is 0.87 of a maximum possible 0.9.

Multivariate Cox Proportional Hazards Analyses: lasso selection

[00143] Lasso variable selection was utilized as the second method of multivariate Cox Proportional Hazards analyses to predict Gestational Age of time to event (preeclampsia), including Gestational age on the day of specimen collection as a covariate. Lasso regression models estimate regression coefficients using penalized optimization methods, where the penalty discourages the model from considering large regression coefficients since we usually believe such large values are not very likely. As a result, some regression coefficients are forced to be zero (i.e., excluded from the model). Here, the resulting model included analytes with non-zero regression coefficients only. The number of these analytes (with nonzero regression coefficients) depends on the severity of the penalty. Cross-validation was used to choose an optimum penalty level. Table 4 shows the results. The coefficient of determination (R²) for the lasso model is 0.53 of a maximum possible 0.9.

Univariate ROC analysis o f preeclampsia as a binary categorical dependent variable

[00144] Univariate analyses was used to discriminate preeclampsia subjects from non- preeclampsia subjects (preeclampsia as a binary categorical variable) as estimated by area under the receiver operating characteristic (ROC) curve. Table 5 shows the area under the ROC curve for the 196 transitions with the highest ROC area of 0.6 or greater.

Multivariate analysis of preeclampsia as a binary categorical dependent variable

[00145] Multivariate analyses was performed to predict preeclampsia as a binary categorical dependent variable, using random forest, boosting, lasso, and logistic regression models. Random forest and boosting models grow many classification trees. The trees vote on the assignment of each subject to one of the possible classes. The forest chooses the class with the most votes over all the trees.

[00146] For each of the four methods (random forest, boosting, lasso, and logistic regression) each method was allowed to select and rank its own best 15 transitions. We then built models with 1 to 15 transitions. Each method sequentially reduces the number of nodes from 15 to 1 independently. A recursive option was used to reduce the number nodes at each step: To determine which node to be removed, the nodes were ranked at each step based on their importance from a nested cross-validation procedure. The least important node was eliminated. The importance measures for lasso and logistic regression are z-values. For random forest and boosting, the variable importance was calculated from permuting out-of- bag data: for each tree, the classification error rate on the out-of-bag portion of the data was recorded; the error rate was then recalculated after permuting the values of each variable (i.e., transition); if the transition was in fact important, there would have been be a big difference between the two error rates; the difference between the two error rates were then averaged over all trees, and normalized by the standard deviation of the differences. The AUCs for these models are shown in Table 6 and in Figure 1 , as estimated by 100 rounds of bootstrap resampling. Table 7 shows the top 15 transitions selected by each multivariate method, ranked by importance for that method. These multivariate analyses suggest that models that combine 2 or more transitions give AUC greater than 0.9, as estimated by bootstrap.

[00147] In multivariate models, random forest (rf) and lasso models gave the best area under the ROC curve as estimated by bootstrap. The following transitions were selected by these two models for having high univariate ROC's:.

FSVVYAK_407.23_579.4

SPELQAEAK 486.75 788.4

VNHVTLSQPK 561.82 673.4

SS PHSPIVEEFQVPYNK_729.36_261.2

SS PHSPIVEEFQVPYNK_729.36_521.3

VVGGLVALR 442.29 784.5 [00148] In summary, univariate and multivariate Cox analyses were performed using transitions collected in Example 1 to predict Gestational Age at Birth, including Gestational age on the day of specimen collection as a covariate. In the univariate Cox analyses, 8 proteins were identified with multiple transitions with p-value less than 0.05. In multivariate Cox analyses, stepwise AIC variable analysis selected 4 transitions, while the lasso model selected 2 transitions. Univariate (ROC) and multivariate (random forest, boosting, lasso, and logistic regression) analyses were performed to predict preeclampsia as a binary categorical variable. Univariate analyses identify 78 analytes with AUROC of 0.7 or greater and 196 analytes with AUROC of 0.6 or greater. Multivariate analyses suggest that models that combine 2 or more transitions give AUC greater than 0.9, as estimated by bootstrap.

[00149] From the foregoing description, it will be apparent that variations and

[00150] The recitation of a listing of elements in any definition of a variable herein includes definitions of that variable as any single element or combination (or

[00151] All patents and publications mentioned in this specification are herein incorporated by reference to the same extent as if each independent patent and publication was specifically and individually indicated to be incorporated by reference.

Example 3. Study II Shotgun Identification of Preeclampsia Biomarkers

[00152] A further study used a hypothesis-independent shotgun approach to identify and quantify additional biomarkers not present on our multiplexed hypothesis dependent MRM assay. Samples were processed as described in the preceding Examples unless noted below.

[00153] Serum samples were depleted of the 14 most abundant serum samples by MARS 14 as described in Example 1. Depleted serum was then reduced with dithiothreitol, alkylated with iodacetamide, and then digested with trypsin at a 1 :20 trypsin to protein ratio overnight at 37°C. Following trypsin digestion, the samples were desalted on an Empore C18 96-well Solid Phase Extraction Plate (3M Company) and lyophilized to dryness. The desalted samples were resolubilized in a reconstitution solution containing five internal standard peptides.

[00154] Tryptic digests of MARS depleted patient (preeclampsia cases and normal pregnancycontrols) samples were fractionated by two-dimensional liquid chromatography and analyzed by tandem mass spectrometry. Aliquots of the samples, equivalent to 3-4 μΐ of serum, were injected onto a 6 cm x 75 μηι self-packed strong cation exchange (Luna SCX, Phenomenex) column. Peptides were eluded from the SCX column with salt (15, 30, 50, 70, and 100% B, where B = 250mM ammonium acetate, 2% acetonitrile, 0.1% formic acid in water) and consecutively for each salt elution, were bound to a 0.5 μΐ CI 8 packed stem trap (Optimize Technologies, Inc.) and further fractionated on a 10 cm x 75 μηι reversed phase ProteoPep II PicoFrit column (New Objective). Peptides were eluted from the reversed phase column with an acetonitrile gradient containing 0.1% formic acid and directly ionized on an LTQ-Orbitrap (ThermoFisher). For each scan, peptide parent ion masses were obtained in the Orbitrap at 60K resolution and the top seven most abundant ions were fragmented in the LTQ to obtain peptide sequence information.

[00155] Parent and fragment ion data were used to search the Human RefSeq database using the Sequest (Eng et al., J. Am. Soc. Mass Spectrom 1994; 5:976-989) and X!Tandem (Craig and Beavis, Bioinformatics 2004; 20: 1466-1467) algorithms. For Sequest, data was searched with a 20 ppm tolerance for the parent ion and 1 AMU for the fragment ion. Two missed trypsin cleavages were allowed, and modifications included static cysteine carboxyamidomethylation and methionine oxidation. After searching the data was filtered by charge state vs. Xcorr scores (charge +1 > 1.5 Xcorr, charge +2 > 2.0, charge +3 > 2.5). Similar search parameters were used for X!tandem, except the mass tolerance for the fragment ion was 0.8 AMU and there is no Xcorr filtering. Instead, the PeptideProphet algorithm (Keller et al, Anal. Chem 2002;74:5383-5392) was used to validate each X! Tandem peptide-spectrum assignment and protein assignments were validated using ProteinProphet algorithm (Nesvizhskii et ah, Anal. Chem 2002; 74:5383-5392). Data was filtered to include only the peptide-spectrum matches that had PeptideProphet probability of 0.9 or more. After compiling peptide and protein identifications, spectral count data for each peptide were imported into DAnTE software (Polpitiya et al., Bioinformatics. 2008;

24: 1556-1558). Log transformed data was mean centered and missing values were filtered, by requiring that a peptide had to be identified in at least 2 cases and 2 controls. To determine the significance of an analyte, Receiver Operating Characteristic (ROC) curves for each analyte were created where the true positive rate (Sensitivity) is plotted as a function of the false positive rate (1 -Specificity) for different thresholds that separate the SPTB and Term groups. The area under the ROC curve (AUC) is equal to the probability that a classifier will rank a randomly chosen positive instance higher than a randomly chosen negative one.

Peptides with AUC greater than or equal to 0.6 identified by both approaches are found in Table 8 and those found uniquely by Sequest or Xtandem are found in Tables 9 and 10, respectively.

[00156] The differentially expressed proteins identified by the hypothesis-independent strategy above, not already present in our MRM-MS assay, were candidates for incorporation into the MRM-MS assay. Candidates were prioritized by AUC and biological function, with preference given for new pathways. Sequences for each protein of interest, were imported into Skyline software which generated a list of tryptic peptides, m/z values for the parent ions and fragment ions, and an instrument-specific collision energy (McLean et al. Bioinformatics (2010) 26 (7): 966-968.McLean et al. Anal. Chem (2010) 82 (24): 101 16-10124).

[00157] The list was refined by eliminating peptides containing cysteines and methionines, where possible, and by using the shotgun data to select the charge state(s) and a subset of potential fragment ions for each peptide that had already been observed on a mass spectrometer.

[00158] After prioritizing parent and fragment ions, a list of transitions was exported with a single predicted collision energy. Approximately 100 transitions were added to a single MRM run. For development, MRM data was collected on either a QTRAP 5500 (AB Sciex) or a 6490 QQQ (Agilent). Commercially available human female serum (from pregnant and non-pregnant donors), was depleted and processed to tryptic peptides, as described above, and used to "scan" for peptides of interest. For development, peptides from the digested serum were separated with a 15 min acetonitrile.e gradient at 100 ul/min on a 2.1 x 50 mM Poroshell 120 EC-C18 column (Agilent) at 40°C.

[00159] The MS/MS data was imported back into Skyline, where all chromatograms for each peptide were overlayed and used to identify a concensus peak corresponding to the peptide of interest and the transitions with the highest intensities and the least noise. Table 11 , contains a list of the most intensely observed candidate transitions and peptides for transfer to the MRM assay.

[00160] Next, the top 2-10 transitions per peptide and up to 7 peptides per protein were selected for collision energy (CE) optimization on the Agilent 6490. Using Skyline or MassHunter Qual software, the optimized CE value for each transition was determined based on the peak area or signal to noise. The two transitions with the largest peak areas per peptide and at least two peptides per protein were chosen for the final MRM method.

Substitutions of transitions with lower peak areas were made when a transition with a larger peak area had a high background level or had a low m/z value that has more potential for interference.

[00161] Lastly, the retention times of selected peptides were mapped using the same column and gradient as our established sMRM assay. The newly discovered analytes were subsequently added to the sMRM method and used in a further hypothesis-dependent discovery study described in Example 4 below.

[00162] The above method was typical for most proteins. However, in some cases, the differentially expressed peptide identified in the shotgun method did not uniquely identify a protein, for example, in protein families with high sequence identity. In these cases, a MRM method was developed for each family member. Also, let it be noted that, for any given protein, peptides in addition to those found to be significant and fragment ions not observed on the Orbitrap may have been included in MRM optimization and added to the final sMRM method if those yielded the best signal intensities. In some cases, transition selection and CEs were re-optmized using purified, synthetic peptides.

Table 8. Preeclampsia: Peptides significant with AUC > 0.6 by X!Tandem and

Protein Uniprot ID (name) Peptide XT AUC S AUC description

alpha- 1 - P01011 K.ITLLSALVETR.T 0.68 0.70 antichymotrypsin (AACT HUMAN)

alpha- 1 - P01011 R.LYGSEAFATDFQDSAAA 0.70 0.78 antichymotrypsin (AACT HUMAN) K.

alpha- 1 - P01011 R.NLAVSQVVHK.A 0.81 0.79 antichymotrypsin (AACT HUMAN)

alpha- 1B- P04217 R.CEGPIPDVTFELLR.E 0.78 0.60 glycoprotein (A1BG HUMAN)

alpha- 1B- P04217 R.LHDNQNGWS GD S APVEL 0.72 0.66 glycoprotein (A 1 B G_HUM AN) ILSDETLPAPEFSPEPESGR.

A

alpha- 1B- P04217 R.CEGPIPDVTFELLR.E 0.64 0.60 glycoprotein (A1BG HUMAN)

alpha- 1B- P04217 R.TPGAAANLELIFVGPQHA 0.71 0.67 glycoprotein (A1BG HUMAN) GNYR.C

alpha- 1B- P04217 K.LLELTGPK.S 0.70 0.66 glycoprotein (A1BG HUMAN)

alpha- 1B- P04217 R.ATWSGAVLAGR.D 0.84 0.74 glycoprotein (A1BG HUMAN)

alpha-2- P08697 K.HQM*DLVATLSQLGLQE 0.67 0.67 antiplasmin (A2AP HUMAN) LFQAPDLR.G

alpha-2- P08697 K.LGNQEPGGQTALK.S 0.83 0.83 antiplasmin (A2AP HUMAN)

alpha-2- P08697 K.GFPIKEDFLEQSEQLFGA 0.68 0.65 antiplasmin (A2AP HUMAN) KPVSLTGK.Q

alpha-2-HS- P02765 R.QPNCDDPETEEAALVAID 0.61 0.61 glycoprotein (FETUA HUMAN YINQNLP WGYK. H

preproprotein )

alpha-2-HS- P02765 K.VWPQQPSGELFEIEIDTL 0.79 0.67 glycoprotein (FETUA HUMAN ETTCHVLDPTPVAR.C

preproprotein )

alpha-2-HS- P02765 K.EHAVEGDCDFQLLK.L 0.90 0.77 glycoprotein (FETUA HUMAN

preproprotein )

alpha-2-HS- P02765 R.QPNCDDPETEEAALVAID 0.63 0.61 glycoprotein (FETUA HUMAN YINQNLP WGYK. H

preproprotein )

alpha-2-HS- P02765 K.HTLNQIDEVK.V 0.70 0.68 glycoprotein (FETUA HUMAN

preproprotein )

alpha-2-HS- P02765 R.TWQPSVGAAAGPVVPP 0.83 0.83 glycoprotein (FETUA HUMAN CPGR.I

preproprotein )

angiotensinogen P01019 K.TGCSLMGASVDSTLAFN 0.75 0.67 preproprotein (ANGT HUMAN) TYVHFQGK.M

angiotensinogen P01019 R.AAM*VGMLANFLGFR.I 0.65 0.63 preproprotein (ANGT HUMAN) Protein Uniprot ID (name) Peptide XT AUC S AUC description

angiotensinogen P01019 R.AAMVGMLANFLGFR.I 0.65 0.64 preproprotein (ANGT HUMAN)

angiotensinogen P01019 R.AAM*VGM*LANFLGFR.I 0.65 0.65 preproprotein (ANGT HUMAN)

angiotensinogen P01019 R.AAMVGM*LANFLGFR.I 0.65 0.74 preproprotein (ANGT HUMAN)

angiotensinogen P01019 K. QPF VQGLAL YTP VVLPR. 0.60 0.74 preproprotein (ANGT HUMAN) S

angiotensinogen P01019 R.AAM*VGMLANFLGFR.I 0.64 0.63 preproprotein (ANGT HUMAN)

angiotensinogen P01019 R.AAMVGMLANFLGFR.I 0.64 0.64 preproprotein (ANGT HUMAN)

angiotensinogen P01019 R.AAM*VGM*LANFLGFR.I 0.64 0.65 preproprotein (ANGT HUMAN)

angiotensinogen P01019 R.AAMVGM*LANFLGFR.I 0.64 0.74 preproprotein (ANGT HUMAN)

angiotensinogen P01019 K.VLSALQAVQGLLVAQGR 0.74 0.77 preproprotein (ANGT HUMAN) .A

angiotensinogen P01019 K.QPFVQGLAL YTP VVLPR. 0.75 0.74 preproprotein (ANGT HUMAN) S

angiotensinogen P01019 R. AD SQAQLLLSTVVGVFT 0.78 0.77 preproprotein (ANGT HUMAN) APGLHLK.Q

antithrombin-III P01008 R.ITDVIPSEAINELTVLVLV 0.78 0.78

(ANT3 HUMAN) NTIYFK.G

antithrombin-III P01008 K.NDNDNIFLSPLSISTAFA 0.87 0.83

(ANT3 HUMAN) MTK.L

antithrombin-III P01008 R.EVPLNTIIFMGR.V 0.69 0.62

(ANT3 HUMAN)

antithrombin-III P01008 R.EVPLNTIIFM * GR. V 0.69 0.69

(ANT3 HUMAN)

antithrombin-III P01008 R.VAEGTQVLELPFKGDDIT 0.83 0.92

(ANT3 HUMAN) M*VLILPKPEK.S

antithrombin-III P01008 R.VAEGTQVLELPFKGDDIT 0.83 0.96

(ANT3 HUMAN) MVLILPKPEK.S

antithrombin-III P01008 K.EQLQDMGLVDLFSPEK.S 0.85 0.86

(ANT3 HUMAN)

antithrombin-III P01008 R.VAEGTQVLELPFKGDDIT 0.94 0.92

(ANT3 HUMAN) M*VLILPKPEK.S

antithrombin-III P01008 R.VAEGTQVLELPFKGDDIT 0.94 0.96

(ANT3 HUMAN) MVLILPKPEK.S

antithrombin-III P01008 R.EVPLNTIIFMGR.V 0.63 0.62

(ANT3 HUMAN)

antithrombin-III P01008 R.EVPLNTIIFM * GR. V 0.63 0.69

(ANT3 HUMAN)

antithrombin-III P01008 R.DIPMNPMCIYR. S 0.71 0.70

(ANT3 HUMAN)

apolipoprotein P02652 K.EPCVESLVSQYFQTVTD 0.83 0.83

apolipoprotein P04114 K.TEVIPPLIENR.Q 0.62 0.64

Protein Uniprot ID (name) Peptide XT AUC S AUC description

ceruloplasmin P00450 K.M*YYSAVDPTKDIFTGLI 0.66 0.68

(CERU HUMAN) GPM .I

ceruloplasmin P00450 K.M*YYSAVDPTKDIFTGLI 0.66 0.76

(CERU HUMAN) GPM*K.I

ceruloplasmin P00450 R.SGAGTEDSACIPWAYYS 0.95 0.95

(CERU_HUMAN) TVDQVKDLYSGLIGPLIVC

R.R

ceruloplasmin P00450 R.KAEEEHLGILGPQLHAD 0.85 0.77

(CERU HUMAN) VGD VK.I

ceruloplasmin P00450 K.EVGPTNADPVCLAK.M 0.62 0.77

(CERU HUMAN)

ceruloplasmin P00450 R.MYSVNGYTFGSLPGLSM 0.63 0.71

(CERU HUMAN) CAEDR.V

ceruloplasmin P00450 K.DIASGLIGPLIIC .K 0.63 0.66

(CERU HUMAN)

ceruloplasmin P00450 R.QKDVDKEFYLFPTVFDE 0.64 0.66

(CERU HUMAN) NESLLLEDNIR.M

ceruloplasmin P00450 R. GPEEEHLGILGPVI WAEV 0.65 0.61

(CERU HUMAN) GDTIR.V

ceruloplasmin P00450 K.M*YYSAVDPTKDIFTGLI 0.67 0.68

(CERU HUMAN) GPMK.I

ceruloplasmin P00450 K.M*YYSAVDPTKDIFTGLI 0.67 0.76

(CERU HUMAN) GPM*K.I

ceruloplasmin P00450 K.M*YYSAVDPTKDIFTGLI 0.67 0.68

(CERU HUMAN) GPMK.I

ceruloplasmin P00450 K.M*YYSAVDPTKDIFTGLI 0.67 0.76

(CERU HUMAN) GPM*K.I

ceruloplasmin P00450 K.GAYPLSIEPIGVR.F 0.67 0.63

(CERU HUMAN)

ceruloplasmin P00450 R.GVYSSDVFDIFPGTYQTL 0.67 0.67

(CERU HUMAN) EM*FPR.T

ceruloplasmin P00450 K.DIASGLIGPLIICKK.D 0.67 0.73

(CERU HUMAN)

ceruloplasmin P00450 R.SGAGTEDSACIPWAYYS 0.70 0.70

(CERU HUMAN) TVDQVK.D

ceruloplasmin P00450 R.IYHSHIDAPK.D 0.77 0.76

(CERU HUMAN)

ceruloplasmin P00450 R.ADDKVYPGEQYTYMLL 0.77 0.80

(CERU HUMAN) ATEEQSPGEGDGNCVTR.I

ceruloplasmin P00450 K.DLYSGLIGPLIVCR.R 0.78 0.82

(CERU HUMAN)

ceruloplasmin P00450 R.TTIEKPVWLGFLGPIIK.A 0.88 0.85

(CERU HUMAN)

cholinesterase P06276 K.IFFPGVSEFGK.E 0.87 0.76

(CHLE HUMAN)

cholinesterase P06276 R.AILQSGSFNAPWAVTSLY 1.00 0.83

(CHLE HUMAN) EAR.N Protein Uniprot ID (name) Peptide XT AUC S AUC description

coagulation P00748 R.LHEAFSPVSYQHDLALL 0.72 0.76 factor XII (FA12 HUMAN) R.L

coagulation P05160 R.GDTYPAELYITGSILR.M 0.67 0.83 factor XIII B (F 13 B_HUM AN)

chain

coagulation P05160 K.VLHGDLIDFVCK.Q 0.69 0.60 factor XIII B (F13B_HUMAN)

chain

complement Clr P00736 K.LVFQQFDLEPSEGCFYD 0.69 0.66 subcomponent (C1R HUMAN) YVK.I

complement Cls P09871 R. VKNYVD WIMK. T 0.69 0.60 subcomponent (CIS HUMAN)

complement Cls P09871 K.SNALDIIFQTDLTGQK.K 0.75 0.70 subcomponent (CIS HUMAN)

complement C2 P06681 R.DFHINLFR.M 0.75 0.72

(C02 HUMAN)

complement C2 P06681 R.GALISDQWVLTAAHCFR. 0.60 0.75

(C02 HUMAN) D

complement C2 P06681 K.KNQGILEFYGDDIALLK. 0.62 0.67

(C02 HUMAN) L

complement C3 P01024 R.IHWESASLLR.S 0.80 0.77

(C03 HUMAN)

complement C4- P0C0L5 R.VHYTVCIWR.N 0.67 0.65 B-like (C04B HUMAN)

preproprotein

complement C4- P0C0L5 K.AEMADQAAAWLTR.Q 0.78 0.89 B-like (C04B HUMAN)

preproprotein

complement C4- P0C0L5 K.M*RPSTDTITVMVENSH 0.65 0.65 B-like (C04B HUMAN) GLR.V

preproprotein

complement C4- P0C0L5 K.MRPSTDTITVMVENSHG 0.65 0.72 B-like (C04B HUMAN) LR.V

preproprotein

complement C4- P0C0L5 R.VQQPDCREPFLSCCQFAE 0.67 0.60 B-like (C04B HUMAN) SLRK.K

preproprotein

complement C4- P0C0L5 K.LVNGQSHISLSK.A 0.73 0.73 B-like (C04B HUMAN)

preproprotein

complement C4- P0C0L5 R.GQIVFMNREPK.R 0.80 0.62 B-like (C04B HUMAN)

preproprotein

complement C4- P0C0L5 K.VGLSGM*AIADVTLLSGF 0.80 0.80 B-like (C04B HUMAN) HALR.A

preproprotein

complement C4- P0C0L5 K.VGLSGMAIADVTLLSGF 0.80 0.83 Protein Uniprot ID (name) Peptide XT AUC S AUC description

B-like (C04B HUMAN) HALR.A

preproprotein

complement C4- P0C0L5 R.GHLFLQTDQPIYNPGQR. 0.70 0.68 B-like (C04B HUMAN) V

preproprotein

complement C4- P0C0L5 K.M*RPSTDTITVMVENSH 0.75 0.65

B-like (C04B HUMAN) GLR.V

preproprotein

complement C4- P0C0L5 K.MRPSTDTITVMVENSHG 0.75 0.72

B-like (C04B HUMAN) LR.V

preproprotein

complement C4- P0C0L5 K.SHALQL R.Q 0.76 0.70 B-like (C04B HUMAN)

preproprotein

complement C4- P0C0L5 R.YVSHFETEGPHVLLYFDS 0.88 0.89

B-like (C04B HUMAN) VPTSR.E

preproprotein

complement C4- P0C0L5 R.GSSTWLTAFVL .V 0.61 0.72

B-like (C04B HUMAN)

preproprotein

complement C4- P0C0L5 R.YIYGKPVQGVAYVR.F 0.63 0.73 B-like (C04B HUMAN)

preproprotein

complement C4- P0C0L5 K.SCGLHQLLR.G 0.65 0.65 B-like (C04B HUMAN)

preproprotein

complement C4- P0C0L5 R.GPEVQLVAHSPWLK.D 0.69 0.73 B-like (C04B HUMAN)

preproprotein

complement C4- P0C0L5 R.KKEVYM *PS SIFQDDFVI 0.70 0.67 B-like (C04B HUMAN) PDISEPGTWK.I

preproprotein

complement C4- P0C0L5 R.KKEVYMPSSIFQDDFVIP 0.70 0.69 B-like (C04B HUMAN) DISEPGTWK.I

preproprotein

complement C4- P0C0L5 R.VQQPDCREPFLSCCQFAE 0.76 0.74 B-like (C04B HUMAN) SLR.K

preproprotein

complement C4- P0C0L5 K.VGLSGM*AIADVTLLSGF 0.80 0.80

B-like (C04B HUMAN) HALR.A

preproprotein

complement C4- P0C0L5 K. VGLS GMAI AD VTLLS GF 0.80 0.83

B-like (C04B HUMAN) HALR.A

preproprotein

complement C4- P0C0L5 K.ASAGLLGAHAAAITAYA 0.85 0.83

B-like (C04B HUMAN) LTLTK.A

preproprotein Protein Uniprot ID (name) Peptide XT AUC S AUC description

complement C5 P01031 K.ITHYNYLILSK.G 0.73 0.73 preproprotein (C05 HUMAN)

complement C5 P01031 R.KAFDICPLVK.I 0.83 0.87 preproprotein (C05 HUMAN)

complement C5 P01031 R.IPLDLVPK.T 0.90 0.63 preproprotein (C05 HUMAN)

complement C5 P01031 R.MVETTAYALLTSLNLKD 0.92 0.75 preproprotein (C05 HUMAN) INYVNPVIK.W

complement C5 P01031 K. ALLVGEHLNII VTPK. S 1.00 0.87 preproprotein (C05 HUMAN)

complement C5 P01031 K.LKEGMLSIMSYR.N 0.62 0.75 preproprotein (C05 HUMAN)

complement C5 P01031 R.YIYPLDSLTWIEYWPR.D 0.70 0.69 preproprotein (C05 HUMAN)

complement C5 P01031 K.GGSASTWLTAFALR.V 0.63 0.83 preproprotein (C05 HUMAN)

complement C5 P01031 R.YGGGFYSTQDTINAIEGL 0.73 0.74 preproprotein (C05 HUMAN) TEYSLLVK.Q

complement P13671 K.AKDLHLSDVFLK.A 0.63 0.62 component C6 (C06 HUMAN)

complement P13671 K.ALNHLPLEYNSALYSR.I 0.60 0.62 component C6 (C06 HUMAN)

complement PI 0643 R.LSGNVLSYTFQVK.I 0.71 0.63 component C7 (C07 HUMAN)

complement P07357 R.KDDIMLDEGMLQSLMEL 0.78 0.89 component C8 (C08A_HUMAN) PDQYNYGMYAK.F

alpha chain

complement P07358 R.DFGTHYITEAVLGGIYEY 0.80 0.73 component C8 (CO 8B HUMAN) TLVMNK.E

beta chain

preproprotein

complement P07358 R.DTMVEDLVVLVR.G 0.88 0.76 component C8 (CO 8B HUMAN)

beta chain

preproprotein

complement P07358 R. YY AGGC SPHYILNTR. F 0.70 0.71 component C8 (CO 8B HUMAN)

beta chain

preproprotein

complement P07360 R.SLPVSDSVLSGFEQR.V 0.79 0.81 component C8 (C08G_HUMAN)

gamma chain

complement P07360 R.VQEAHLTEDQIFYFPK.Y 0.98 0.84 component C8 (C08G_HUMAN)

gamma chain

complement P02748 R.TAGYGINILGMDPLSTPF 0.62 0.64 component C9 (C09 HUMAN) DNEFYNGLCNR.D Protein Uniprot ID (name) Peptide XT AUC S AUC description

complement P02748 R.RPWNVASLIYETK. G 0.60 0.74 component C9 (C09 HUMAN)

complement P02748 R.AIEDYINEFSVRK. C 0.67 0.67 component C9 (C09 HUMAN)

complement P02748 R.AIEDYINEFSVR.K 0.77 0.79 component C9 (C09 HUMAN)

complement P00751 R.LEDSVTYHCSR.G 0.60 0.60 factor B (CFAB_HUMAN)

preproprotein

complement P00751 R.FIQVGVISWGVVDVCK.N 0.67 0.79 factor B (CFAB_HUMAN)

preproprotein

complement P00751 R.DFHINLFQVLPWLK.E 0.78 0.76 factor B (CFAB_HUMAN)

preproprotein

complement P00751 K.YGQTIRPICLPCTEGTTR. 0.60 0.70 factor B (CFAB_HUMAN) A

preproprotein

complement P00751 R.LLQEGQALEYVCPSGFY 0.74 0.74 factor B (CFAB_HUMAN) PYPVQTR.T

preproprotein

complement P08603 R.RPYFPVAVGK.Y 0.67 0.70 factor H (CFAH HUMAN)

complement P08603 K.CTSTGWIPAPR.C 0.70 0.66 factor H (CFAH HUMAN)

complement P08603 K.CLHPCVISR.E 0.94 0.64 factor H (CFAH HUMAN)

complement P08603 R.EIMENYNIALR.W 0.67 0.71 factor H (CFAH HUMAN)

complement P08603 K.CLHPCVISR.E 0.75 0.64 factor H (CFAH HUMAN)

complement P08603 K.AVYTCNEGYQLLGEINY 0.73 0.62 factor H (CFAH HUMAN) R.E

complement P08603 R.SITCIHGVWTQLPQCVAI 0.61 0.61 factor H (CFAH HUMAN) DK.L

complement P08603 R.WQSIPLCVEK.I 0.65 0.65 factor H (CFAH HUMAN)

complement P08603 K.TDCLSLPSFENAIPMGEK. 0.74 0.77 factor H (CFAH HUMAN) K

complement P08603 K.CFEGFGIDGPAIAK.C 0.76 0.69 factor H (CFAH HUMAN)

complement P08603 K.CFEGFGIDGPAIAK.C 0.83 0.69 factor H (CFAH HUMAN)

complement P08603 K.IDVHLVPDR.K 0.61 0.67 factor H (CFAH HUMAN)

complement P08603 K.SSNLIILEEHLK.N 0.77 0.69 factor H (CFAH HUMAN) Protein Uniprot ID (name) Peptide XT AUC S AUC description

complement P05156 R.AQLGDLPWQVAIK.D 0.66 0.69 factor I (CFAI_HUMAN)

preproprotein

complement P05156 R.VFSLQWGEVK.L 0.69 0.77 factor I (CFAI_HUMAN)

preproprotein

corticosteroid- P08185 R.WSAGLTSSQVDLYIP .V 0.63 0.61 binding globulin (CBG HUMAN)

fibrinogen alpha P02671 K.TFPGFFSPMLGEFVSETE 0.80 0.78 chain (FIBA HUMAN) SR.G

gelsolin P06396 R.IEGSNKVPVDPATYGQF 0.78 0.78

(GELS HUMAN) YGGD S YIILYNYR. H

gelsolin P06396 R.AQPVQVAEGSEPDGFWE 0.62 0.65

(GELS HUMAN) ALGGK.A

gelsolin P06396 K.TPSAAYLWVGTGASEAE 0.78 0.78

(GELS HUMAN) KTGAQELLR.V

gelsolin P06396 R.VEKFDLVPVPTNLYGDF 0.61 0.63

(GELS HUMAN) FTGDAYVILK.T

gelsolin P06396 R.EVQGFESATFLGYFK.S 0.87 0.88

(GELS HUMAN)

gelsolin P06396 K.NWRDPDQTDGLGLSYLS 0.89 0.89

(GELS HUMAN) SHIANVER.V

gelsolin P06396 K.TPSAAYLWVGTGASEAE 0.87 0.77

(GELS HUMAN) K.T

glutathione P22352 K.FLVGPDGIPIMR.W 0.85 0.77 peroxidase 3 (GPX3 HUMAN)

hemopexin P02790 R.LEKEVGTPHGIILDSVDA 0.93 0.74

(HEMO HUMAN) AFICPGSSR.L

hemopexin P02790 R.WKNFPSPVDAAFR.Q 0.64 0.82

(HEMO HUMAN)

hemopexin P02790 R.GECQAEGVLFFQGDREW 0.60 0.64

(HEMO HUMAN) FWDLATGTMK.E

hemopexin P02790 R.GECQAEGVLFFQGDREW 0.60 0.83

(HEMO HUMAN) FWDLATGTM*K.E

hemopexin P02790 R.GECQAEGVLFFQGDREW 0.93 0.64

(HEMO HUMAN) FWDLATGTMK.E

hemopexin P02790 R.GECQAEGVLFFQGDREW 0.93 0.83

(HEMO HUMAN) FWDLATGTM*K.E

hemopexin P02790 K.EVGTPHGIILDSVDAAFI 0.62 0.69

(HEMO HUMAN) CPGSSR.L

hemopexin P02790 R.LWWLDLK.S 0.64 0.64

(HEMO HUMAN)

hemopexin P02790 K.NFPSPVDAAFR.Q 0.65 0.72

(HEMO HUMAN)

hemopexin P02790 R.EWFWDLATGTMK.E 0.68 0.65

(HEMO HUMAN)

hemopexin P02790 K.GGYTLVSGYPK.R 0.69 0.65 Protein Uniprot ID (name) Peptide XT AUC S AUC description

(HEMO HUMAN)

hemopexin P02790 K.LYLVQGTQVYVFLTK. G 0.69 0.76

(HEMO HUMAN)

heparin cofactor P05546 R.EYYFAEAQIADFSDPAFI 0.80 0.78 2 (HEP2 HUMAN) SK.T

heparin cofactor P05546 K.QFPILLDFK.T 0.62 1.00 2 (HEP2 HUMAN)

heparin cofactor P05546 K.QFPILLDFK.T 0.64 1.00 2 (HEP2 HUMAN)

heparin cofactor P05546 K.FAFNLYR.V 0.70 0.60

2 (HEP2 HUMAN)

histidine-rich P04196 R.DGYLFQLLR.I 0.65 0.65 glycoprotein (HRG HUMAN)

insulin-like P35858 R. SFEGLGQLEVLTLDHNQ 0.75 0.83 growth factor- (ALS_HUMAN) LQEVK.A

binding protein

complex acid

labile subunit

insulin-like P35858 R.TFTPQPPGLER.L 0.75 0.60 growth factor- (ALS_HUMAN)

binding protein

complex acid

labile subunit

insulin-like P35858 R.AFWLDVSHNR.L 0.77 0.75 growth factor- (ALS HUMAN)

binding protein

complex acid

labile subunit

insulin-like P35858 R.LAELPADALGPLQR.A 0.66 0.64 growth factor- (ALS_HUMAN)

binding protein

complex acid

labile subunit

insulin-like P35858 R.LEALPNSLLAPLGR.L 0.70 0.67 growth factor- (ALS_HUMAN)

binding protein

complex acid

labile subunit

insulin-like P35858 R.NLIAAVAPGAFLGLK.A 0.70 0.68 growth factor- (ALS_HUMAN)

binding protein

complex acid

labile subunit

inter-alpha- P19827 R. Q A VDTAVD GVFIR. S 0.60 0.64 trypsin inhibitor (ITIH1 HUMAN)

heavy chain HI

inter-alpha- PI 9827 K.TAFISDFAVTADGNAFIG 0.81 0.86 Protein Uniprot ID (name) Peptide XT AUC S AUC description

trypsin inhibitor (ITIH1 HUMAN) DIK.D

heavy chain HI

inter-alpha- P19827 R.GHMLENHVER.L 0.63 0.61 trypsin inhibitor (ITIH1 HUMAN)

heavy chain HI

inter-alpha- P19827 R.GHM*LENHVER.L 0.63 0.70 trypsin inhibitor (ITIH1 HUMAN)

heavy chain HI

inter-alpha- P19827 K.TAFISDFAVTADGNAFIG 0.75 0.60 trypsin inhibitor (ITIH1 HUMAN) DIKDKVTAWK. Q

heavy chain HI

inter-alpha- P19827 R.GIEILNQVQESLPELSNH 0.80 0.80 trypsin inhibitor (ITIH1 HUMAN) ASILIMLTDGDPTEGVTDR.

heavy chain HI S

inter-alpha- P19827 K.ILGDM^QPGDYFDLVLF 0.85 0.79 trypsin inhibitor (ITIH1 HUMAN) GTR.V

heavy chain HI

inter-alpha- P19827 K.LDAQASFLPK.E 0.88 0.75 trypsin inhibitor (ITIH1 HUMAN)

heavy chain HI

inter-alpha- P19827 R.GFSLDEATNLNGGLLR.G 0.80 0.80 trypsin inhibitor (ITIH1 HUMAN)

heavy chain HI

inter-alpha- P19827 K.TAFISDFAVTADGNAFIG 0.93 0.96 trypsin inhibitor (ITIH1 HUMAN) DIKDK.V

heavy chain HI

inter-alpha- P19827 K.GSLVQASEANLQAAQDF 0.60 0.65 trypsin inhibitor (ITIH1 HUMAN) VR.G

heavy chain HI

inter-alpha- P19827 R. GHMLENHVER. L 0.64 0.61 trypsin inhibitor (ITIH1 HUMAN)

heavy chain HI

inter-alpha- P19827 R.GHM*LENHVER.L 0.64 0.70 trypsin inhibitor (ITIH1 HUMAN)

heavy chain HI

inter-alpha- P19827 R.LWAYLTIQELLAK.R 0.72 0.74 trypsin inhibitor (ITIH1 HUMAN)

heavy chain HI

inter-alpha- P19827 R.EVAFDLEIPK.T 0.78 0.62 trypsin inhibitor (ITIH1 HUMAN)

heavy chain HI

inter-alpha- P19823 R.SILQMSLDHHIVTPLTSL 0.76 0.76 trypsin inhibitor (ITIH2 HUMAN) VIENEAGDER.M

heavy chain H2

inter-alpha- P19823 R. SILQM * SLDHHI VTPLT SL 0.76 0.80 trypsin inhibitor (ITIH2 HUMAN) VIENEAGDER.M

heavy chain H2 Protein Uniprot ID (name) Peptide XT AUC S AUC description

inter-alpha- P19823 R.SILQMSLDHHIVTPLTSL 0.77 0.76 trypsin inhibitor (ITIH2 HUMAN) VIENEAGDER.M

heavy chain H2

inter-alpha- P19823 R. SILQM * SLDHHI VTPLT SL 0.77 0.80 trypsin inhibitor (ITIH2 HUMAN) VIENEAGDER.M

heavy chain H2

inter-alpha- P19823 K.AGELEVFNGYFVHFFAP 0.79 0.76 trypsin inhibitor (ITIH2 HUMAN) DNLDPIP .N

heavy chain H2

inter-alpha- P19823 R.ETAVDGELVVLYDVK.R 0.94 0.97 trypsin inhibitor (ITIH2 HUMAN)

heavy chain H2

inter-alpha- P19823 R.NVQFNYPHTSVTDVTQN 0.74 0.83 trypsin inhibitor (ITIH2 HUMAN) NFHNYFGGSEIVVAGK.F

heavy chain H2

inter-alpha- P19823 R.FLHVPDTFEGHFDGVPVI 0.81 0.81 trypsin inhibitor (ITIH2 HUMAN) S .G

heavy chain H2

inter-alpha- Q14624 K.YIFHNFM*ER.L 0.70 0.73 trypsin inhibitor (ITIH4 HUMAN)

heavy chain H4

inter-alpha- Q14624 R. SFAAGIQALGGTNINDA 0.75 0.75 trypsin inhibitor (ITIH4 HUMAN) MLM AVQLLD S SNQEER. L

heavy chain H4

inter-alpha- Q14624 R.NMEQFQVSVSVAPNAK.I 1.00 1.00 trypsin inhibitor (ITIH4 HUMAN)

heavy chain H4

inter-alpha- Q14624 R.VQGNDHSATR.E 0.85 0.86 trypsin inhibitor (ITIH4 HUMAN)

heavy chain H4

inter-alpha- Q14624 K.WKETLFSVMPGLK.M 0.66 0.69 trypsin inhibitor (ITIH4 HUMAN)

heavy chain H4

inter-alpha- Q14624 K.AGFSWIEVTFK.N 0.78 0.82 trypsin inhibitor (ITIH4 HUMAN)

heavy chain H4

inter-alpha- Q14624 R.DQFNLIVFSTEATQWRPS 0.61 0.60 trypsin inhibitor (ITIH4 HUMAN) LVPASAENVNK.A

heavy chain H4

inter-alpha- Q14624 R.LWAYLTIQQLLEQTVSA 0.66 0.66 trypsin inhibitor (ITIH4 HUMAN) SDADQQALR.N

heavy chain H4

kallistatin P29622 K.FSISGSYVLDQILPR.L 0.79 0.72

(KAIN HUMAN)

kininogen-1 P01042 K. AATGECTAT VGKR. S 0.76 0.60

(K G1 HUMAN)

kininogen-1 P01042 K.ENFLFLTPDCK.S 0.71 0.68 Protein Uniprot ID (name) Peptide XT AUC S AUC description

(KNG1 HUMAN)

kininogen-1 P01042 R.DIPTNSPELEETLTHTITK. 0.65 0.64

(KNG1 HUMAN) L

kininogen-1 P01042 K.IYPTVNCQPLGM*ISLMK 0.66 0.60

(KNG1 HUMAN) .R

kininogen-1 P01042 K.IYPTVNCQPLGMISLMK. 0.66 0.62

(KNG1 HUMAN) R

kininogen-1 P01042 K.IYPTVNCQPLGMISLM*K 0.66 0.63

(KNG1 HUMAN) .R

kininogen-1 P01042 R.IGEIKEETTSHLR.S 0.67 0.70

(KNG1 HUMAN)

kininogen-1 P01042 K. YNS QNQ SNNQF VL YR. I 0.76 0.65

(KNG1 HUMAN)

kininogen-1 P01042 K.TVGSDTFYSFK.Y 0.78 0.77

(KNG1 HUMAN)

leucine-rich P02750 R.DGFDISGNPWICDQNLSD 0.73 0.73 alpha-2- (A2GL HUMAN) LYR.W

glycoprotein

leucine-rich P02750 R.NALTGLPPGLFQASATLD 0.79 0.79 alpha-2- (A2GL HUMAN) TLVLK.E

glycoprotein

leucine-rich P02750 K.ALGHLDLSGNR.L 0.71 0.71 alpha-2- (A2GL HUMAN)

glycoprotein

leucine-rich P02750 R. VAAGAFQGLR. Q 0.71 0.77 alpha-2- (A2GL HUMAN)

glycoprotein

lipopolysacchari PI 8428 R. SPVTLLAAVMSLPEEHN 0.65 0.61 de-binding (LBP HUMAN) K.M

protein

lumican P51884 K.SLEYLDLSFNQIAR.L 0.93 0.96

(LUM HUMAN)

monocyte P08571 R.LTVGAAQVPAQLLVGAL 0.68 0.63 differentiation (CD 14 HUMAN) R.V

antigen CD 14

N- Q96PD5 R.EGKEYGVVLAPDGSTVA 0.64 0.64 acetylmuramoyl- (PGRP2_HUMAN) VEPLLAGLEAGLQGR.R

L-alanine

amidase

N- Q96PD5 K.EFTEAFLGCPAIHPR. C 0.63 0.62 acetylmuramoyl- (PGRP2_HUMAN)

L-alanine

amidase

N- Q96PD5 R.TDCPGDALFDLLR.T 0.88 0.86 acetylmuramoyl- (PGRP2_HUMAN)

L-alanine

amidase

Protein Uniprot ID (name) Peptide XT AUC S AUC description

PREDICTED: P0C0L4 R.GCGEQTMIYLAPTLAAS 0.71 0.67 complement C4- (C04A_HUMAN) R.Y

A

pregnancy zone P20742 R.NELIPLIYLENPR.R 1.00 0.67 protein (PZP HUMAN)

pregnancy zone P20742 K.LEAGINQLSFPLSSEPIQG 1.00 0.73 protein (PZP HUMAN) SYR.V

pregnancy zone P20742 R.NQ GNT WLT AFVLK. T 0.73 0.78 protein (PZP HUMAN)

pregnancy zone P20742 R.AFQPFFVELTMPYSVIR.G 0.83 0.88 protein (PZP HUMAN)

pregnancy zone P20742 R.IQHPFTVEEFVLPK.F 0.65 0.79 protein (PZP HUMAN)

pregnancy zone P20742 K.ALLAYAFSLLG .Q 0.69 0.74 protein (PZP HUMAN)

pregnancy- PI 1464 R.TLFLLGVTK.Y 0.74 0.83 specific beta-1- (PSG1 HUMAN)/

glycoprotein 1 Q9UQ74

/8/4 (PSG8 HUMAN)/

Q00888

(PSG4 HUMAN)

protein AMBP P02760 R.TVAACNLPIVR.G 0.78 0.77 preproprotein (AMBP HUMAN)

protein AMBP P02760 K.WYNLAIGSTCPWLK.K 0.80 0.80 preproprotein (AMBP HUMAN)

protein Z- Q9U 55 K.LILVDYILF . G 0.69 0.62 dependent (ZPI_HUMAN)

protease inhibitor

prothrombin P00734 R.KSPQELLCGASLISDR.W 0.63 0.65 preproprotein (THRB HUMAN)

prothrombin P00734 R.T AT SE YQTFFNPR. T 0.79 0.61 preproprotein (THRB HUMAN)

prothrombin P00734 R.VTGWGNLKETWTANVG 1.00 0.71 preproprotein (THRB HUMAN) K.G

prothrombin P00734 R.IVEGSDAEIGMSPWQVM 0.65 0.61 preproprotein (THRB HUMAN) LFR.K

prothrombin P00734 K.HQDFNSAVQLVENFCR. 0.65 0.64 preproprotein (THRB HUMAN) N

prothrombin P00734 R.IVEGSDAEIGM * SP WQV 0.65 0.80 preproprotein (THRB HUMAN) MLFR.K

prothrombin P00734 R.IVEGSDAEIGMSPWQVM 0.65 1.00 preproprotein (THRB HUMAN) *LFR.K

prothrombin P00734 R.RQEC SIPVCGQDQVTVA 0.74 0.73 preproprotein (THRB HUMAN) MTPR.S

prothrombin P00734 R.LAVTTHGLPCLAWASAQ 0.76 0.80 preproprotein (THRB HUMAN) AK.A

prothrombin P00734 K. GQP S VLQ VVNLPI VERP V 0.76 0.67 Protein Uniprot ID (name) Peptide XT AUC S AUC description

preproprotein (THPvB HUMAN) CK.D

retinol-binding P02753 R.LLNLDGTCADSYSFVFSR 0.70 0.66 protein 4 (RET4 HUMAN) .D

sex hormone- P04278 R.LFLGALPGEDSSTSFCLN 0.72 0.72 binding globulin (SHBG HUMAN) GLWAQGQR.L

sex hormone- P04278 R.TWDPEGVIFYGDTNPKD 0.75 0.76 binding globulin (SHBG HUMAN) DWFMLGLR.D

sex hormone- P04278 R.IALGGLLFPASNLR.L 0.62 0.72 binding globulin (SHBG HUMAN)

sex hormone- P04278 K. VVLS SGSGPGLDLPLVLG 0.65 0.68 binding globulin (SHBG HUMAN) LPLQLK.L

thyroxine- P05543 K.AVLHIGE .G 0.64 0.75 binding globulin (THBG HUMAN)

thyroxine- P05543 K.GWVDLFVPK.F 0.60 0.61 binding globulin (THBG HUMAN)

thyroxine- P05543 K.FSISATYDLGATLLK.M 0.62 0.64 binding globulin (THBG HUMAN)

thyroxine- P05543 R.SILFLGK.V 0.66 0.63 binding globulin (THBG HUMAN)

transforming Q15582 R.LTLLAPLNSVFK.D 0.78 0.65 growth factor- (BGH3_HUMAN)

beta-induced

protein ig-h3

vitamin D- P02774 K.EYANQFMWEYSTNYGQ 0.67 0.64 binding protein (VTDB HUMAN) APLSLLVS YTK. S

vitamin D- P02774 K.EYANQFM*WEYSTNYG 0.67 0.67 binding protein (VTDB HUMAN) QAPLSLLVS YTK. S

vitamin D- P02774 K.ELPEHTVK.L 0.79 0.74 binding protein (VTDB HUMAN)

vitamin D- P02774 R.RTHLPEVFLSK.V 0.63 0.76 binding protein (VTDB HUMAN)

vitamin D- P02774 K.TAMDVFVCTYFMPAAQ 0.66 0.63 binding protein (VTDB HUMAN) LPELPD VELPTNK. D

vitamin D- P02774 K.LPDATPTELAK.L 0.67 0.73 binding protein (VTDB HUMAN)

vitamin D- P02774 K.EYANQFMWEYSTNYGQ 0.65 0.64 binding protein (VTDB HUMAN) APLSLLVS YTK. S

vitamin D- P02774 K.EYANQFM*WEYSTNYG 0.65 0.67 binding protein (VTDB HUMAN) QAPLSLLVS YTK. S

vitamin D- P02774 K.ELS SFIDKGQELC ADYSE 0.71 0.73 binding protein (VTDB HUMAN) NTFTEYKK.K

vitamin D- P02774 K.EDFTSLSLVLYSR.K 0.71 0.75 binding protein (VTDB HUMAN)

vitamin D- P02774 K.HQPQEFPTYVEPTNDEIC 0.77 0.75 binding protein (VTDB HUMAN) EAFRK.D

vitamin D- P02774 K.HQPQEFPTYVEPTNDEIC 0.60 0.67 binding protein (VTDB HUMAN) EAFR.K Protein Uniprot ID (name) Peptide XT AUC S AUC description

vitamin D- P02774 R.KFPSGTFEQVSQLVK.E 0.62 0.61 binding protein (VTDB HUMAN)

vitamin D- P02774 K.ELS SFIDKGQELC ADYSE 0.64 0.64 binding protein (VTDB HUMAN) NTFTEYK.K

vitamin D- P02774 K.EFSHLGKEDFTSLSLVLY 0.66 0.64 binding protein (VTDB HUMAN) SR.K

vitamin D- P02774 K.SYLSMVGSCCTSASPTV 0.68 0.77 binding protein (VTDB HUMAN) CFLK.E

vitronectin P04004 R.IYISGMAPRPSLAK.K 0.63 0.66

(VTNC HUMAN)

vitronectin P04004 R.IYISGMAPRPSLAK.K 0.64 0.66

(VTNC HUMAN)

vitronectin P04004 K.LIRDVWGIEGPIDAAFTR. 0.81 0.75

(VTNC HUMAN) I

von Willebrand P04275 R.IGWPNAPILIQDFETLPR. 0.67 0.67 factor (VWF HUMAN) E

preproprotein

* = Oxidation of Methionine

[00164] Table 9. Preeclampsia: Additional peptides significant with AUC > 0.6 by Sequest only

Protein description Uniprot ID (name) Peptide S AUC alpha- 1 - P01011 R.EIGELYLPK.F 0.68 antichymotrypsin (AACT_HUMAN)

alpha- 1 - P01011 R.WRDSLEFR.E 0.71 antichymotrypsin (AACT_HUMAN)

alpha- 1 - P01011 K.RLYGSEAFATDFQDSAAA 0.89 antichymotrypsin (AACT_HUMAN) K.K

alpha- 1B- P04217 R.FALVR.E 1.00 glycoprotein (A1BG HUMAN)

alpha- 1B- P04217 R.GVTFLLRR.E 0.67 glycoprotein (A 1 BG HUMAN)

alpha- 1B- P04217 R. RGEKELL VPR. S 0.71 glycoprotein (A 1 BG HUMAN)

alpha- 1B- P04217 K.ELLVPR.S 0.61 glycoprotein (A 1 BG HUMAN)

alpha- 1B- P04217 K.NGVAQEPVHLDSPAIK.H 0.64 glycoprotein (A1BG HUMAN)

alpha-2-antiplasmin P08697 R.NKFDPSLTQR.D 0.60

(A2AP HUMAN)

alpha-2-antiplasmin P08697 R.QLTSGPNQEQVSPLTLLK. 0.67

(A2AP HUMAN) L

alpha-2-antiplasmin P08697 K.HQM*DLVATLSQLGLQEL 0.67

(A2AP HUMAN) FQAPDLR.G

angiotensinogen P01019 R.FM*QAVTGWK.T 0.60 preproprotein (ANGT HUMAN)

angiotensinogen P01019 K.PKDPTFIPAPIQAK.T 0.83 preproprotein (ANGT HUMAN)

angiotensinogen P01019 R. SLDFTELDVAAEK.I 0.60 preproprotein (ANGT HUMAN)

ankyrin repeat and Q8NFD2 R.KNLVPR.D 1.00 protein kinase (ANKK1 HUMAN)

domain-containing

protein 1 Protein description Uniprot ID (name) Peptide S AUC antithrombin-III P01008 R.RVWELS .A 0.68

(ANT3_HUMAN)

apolipoprotein A-IV P06727 K. VKIDQTVEELRR. S 0.62

(APOA4_HUMAN)

apolipoprotein A-IV P06727 K.DLRDKVNSFFSTFK.E 0.92

(APOA4_HUMAN)

apolipoprotein A-IV P06727 K. LVPF ATELHER. L 0.71

(APOA4 HUMAN)

apolipoprotein A-IV P06727 R.RVEPYGENFNK.A 0.86

(APOA4_HUMAN)

apolipoprotein A-IV P06727 K.VNSFFSTF .E 0.87

(APOA4_HUMAN)

apolipoprotein B- P04114 K.AVSM*PSFSILGSDVR.V 0.70 100 (APOB_HUMAN)

apolipoprotein B- P04114 K.AVSMPSFSILGSDVR.V 0.66 100 (APOB HUMAN)

apolipoprotein B- P04114 K.AVSMPSFSILGSDVR.V 0.66 100 (APOB_HUMAN)

apolipoprotein B- P04114 K.AVSM*PSFSILGSDVR.V 0.70 100 (APOB_HUMAN)

apolipoprotein B- P04114 K.VNWEEEAASGLLTSLKD 0.60 100 (APOB_HUMAN) NVPK.A

apolipoprotein B- P04114 R.DLKVEDIPLAR.I 0.70 100 (APOB_HUMAN)

apolipoprotein C-I P02654 K.MREWFSETFQK.V 0.73

(APOC1 HUMAN)

apolipoprotein C-II P02655 K.STAAMSTYTGIFTDQVLS 0.68

(APOC2 HUMAN) VLKGEE.- apolipoprotein E P02649 R.AKLEEQAQQIR.L 0.67

(APOE HUMAN)

apolipoprotein E P02649 R.FWDYLR.W 0.67

(APOE HUMAN) Protein description Uniprot ID (name) Peptide S AUC apolipoprotein E P02649 R.LKSWFEPLVEDMQR.Q 0.65

(APOE HUMAN)

beta-2-glycoprotein P02749 K.VSFFCK.N 0.67 1 (APOH_HUMAN)

beta-2-glycoprotein P02749 R.VCPFAGILENGAVR.Y 0.63 1 (APOH_HUMAN)

beta-2- P61769 K. SNFLNC YVS GFHP SDIEVD 0.60 microglobulin (B2MG HUMAN) LLK.N

biotinidase P43251 R.LSSGLVTAALYGR.L 1.00

(BTD_HUMAN)

carboxypeptidase Q96IY4 K.IAWHVIR.N 0.90 B2 preproprotein (CBPB2 HUMAN)

carboxypeptidase N P22792 K.LSNNALSGLPQGVFGK.L 0.62 subunit 2 (CPN2 HUMAN)

carboxypeptidase N P15169 R.DHLGFQVTWPDESKA 0.93 subunit 2 (CBPN HUMAN)

ceruloplasmin P00450 K.VYVHLK.N 0.67

(CERU_HUMAN)

ceruloplasmin P00450 K.LISVDTEHSNIYLQNGPDR 0.62

(CERU_HUMAN) .1

ceruloplasmin P00450 K.M*YYSAVDPTKDIFTGLIG 0.76

(CERU_HUMAN) PM*K.I

ceruloplasmin P00450 K.M*YYSAVDPTKDIFTGLIG 0.68

(CERU_HUMAN) PMK.I

ceruloplasmin P00450 R. QKD VDKEFYLFPTVFDEN 0.66

(CERU_HUMAN) ESLLLEDNIR.M ceruloplasmin P00450 K.DVDKEFYLFPTVFDENES 0.60

(CERU_HUMAN) LLLEDNIR.M

ceruloplasmin P00450 K.DIFTGLIGPMK.I 0.62

(CERU_HUMAN)

ceruloplasmin P00450 R. S VPP S ASHVAPTETFT YE 0.66

(CERU_HUMAN) WTVPK.E Protein description Uniprot ID (name) Peptide S AUC ceruloplasmin P00450 R. GVYS SDVFDIFPGTYQTLE 0.67

(CERU HUMAN) M*FPR.T

ceruloplasmin P00450 K.DIFTGLIGPM .I 0.62

(CERU HUMAN)

ceruloplasmin P00450 K.VNKDDEEFIESN .M 0.78

(CERU HUMAN)

clusterin PI 0909 R.KYNELLK.S 0.75 preproprotein (CLUS HUMAN)

coagulation factor P00748 R. TTL SGAPC QP WASEAT YR 0.64 XII (FA 12_HUMAN) .N

complement Clq P02745 K.GHIYQGSEADSVFSGFLIF 0.64 subcomponent (C1QA HUMAN) PSA.- subunit A

complement Clq P02747 K.FQSVFTVTR.Q 0.65 subcomponent (C 1 QC HUMAN)

subunit C

complement Clr P00736 R.WILTAAHTLYPK.E 0.68 subcomponent (C1R HUMAN)

complement Clr P00736 K.VLNYVDWIKK.E 0.81 subcomponent (C1R HUMAN)

complement Cls P09871 R. LP VAPLRK. C 0.63 subcomponent (C1S_HUMAN)

complement C2 P06681 R.PICLPCTMEANLALR.R 0.78

(C02_HUMAN)

complement C2 P06681 R. QHLGD VLNFLPL . - 0.70

(C02_HUMAN)

complement C4-B- P0C0L5 K.LGQYASPTAKR.C 0.89 like preproprotein (C04B HUMAN)

complement C4-B- P0C0L5 K.M*RPSTDTITVMVENSHG 0.65 like preproprotein (C04B HUMAN) LR.V

complement C4-B- P0C0L5 K.MRPSTDTITVMVENSHGL 0.72 like preproprotein (C04B HUMAN) R.V Protein description Uniprot ID (name) Peptide S AUC complement C5 P01031 K.EFPYRIPLDLVP .T 0.67 preproprotein (C05_HUMAN)

complement C5 P01031 R.VFQFLEK.S 0.60 preproprotein (C05 HUMAN)

complement C5 P01031 R. MVETTAYALLTSLNLK. D 0.61 preproprotein (C05_HUMAN)

complement C5 P01031 R.ENSLYLTAFTVIGIR.K 0.81 preproprotein (C05 HUMAN)

complement P07357 K.YNPVVIDFEMQPIHEVLR. 0.62 component C8 (C08A HUMAN) H

alpha chain

complement P07358 K.IPGIFELGISSQSDR.G 0.61 component C8 beta (C08B HUMAN)

chain preproprotein

complement P07360 R.RPASPISTIQPK.A 0.71 component C8 (C08G HUMAN)

gamma chain

complement P07360 R.FLQEQGHR.A 0.87 component C8 (C08G HUMAN)

gamma chain

complement factor P00751 K.VSV GGEKR.D 0.60 B preproprotein (CFAB HUMAN)

complement factor P00751 K.CLVNLIEK.V 0.69 B preproprotein (CFAB HUMAN)

complement factor P00751 K. KDNEQHVFK. V 0.68 B preproprotein (CFAB HUMAN)

complement factor P00751 K.ISVIRPSK.G 0.63 B preproprotein (CFAB HUMAN)

complement factor P00751 K. KCL VNLIEK. V 0.63 B preproprotein (CFAB HUMAN)

complement factor P00751 R. LPPTTTCQQQKEELLPAQ 0.64 B preproprotein (CFAB HUMAN) DIK.A Protein description Uniprot ID (name) Peptide S AUC complement factor P00751 K.LQDEDLGFL.- 0.66 B preproprotein (CFAB HUMAN)

complement factor P08603 K. SCDIPVFMNAR.T 0.60 H (CFAH_HUMAN)

complement factor P08603 K.HGGLYHENMR.R 0.75 H (CFAH_HUMAN)

complement factor P08603 K.IIYKENER.F 0.69 H (CFAH HUMAN)

complement factor I P05156 K.RAQLGDLPWQVAIK.D 0.68 preproprotein (CFAI_HUMAN)

conserved Q9Y2V7 K.ISNLL .F 0.71 oligomeric Golgi (COG6 HUMAN)

complex subunit 6

isoform

cornulin Q9UBG3 R.RYARTEGNCTALTR.G 0.81

(CRNN_HUMAN)

FERM domain- Q9BZ67 R.VQLGPYQPGRPAACDLR. 0.63 containing protein 8 (FRMD 8 HUM AN) E

gelsolin P06396 R.VPEARPNSMVVEHPEFLK. 0.61

(GEL S HUMAN) A

gelsolin P06396 K.AGKEPGLQIWR.V 0.70

(GEL S HUMAN)

glucose-induced Q9NWU2 K.VWSEVNQAVLDYENRES 0.83 degradation protein (GID8_HUMAN) TPK.L

8 homolog

hemK Q9Y5R4 R.M*LWALLSGPGRRGSTR. 0.61 methyltransferase (HEMK1 HUMAN) G

family member 1

hemopexin P02790 R.ELISER.W 0.82

(HEMO HUMAN)

hemopexin P02790 R.DVRDYFM*PCPGR.G 0.70

(HEMO HUMAN) Protein description Uniprot ID (name) Peptide S AUC hemopexin P02790 K.GDKVWVYPPEKK.E 0.71

(HEMO HUMAN)

hemopexin P02790 R.DVRDYFMPCPGR.G 0.60

(HEMO HUMAN)

hemopexin P02790 R. EWF WDLATGTMK. E 0.65

(HEMO HUMAN)

hemopexin P02790 R.YYCFQGNQFLR.F 0.68

(HEMO HUMAN)

hemopexin P02790 R.RLWWLDLK.S 0.65

(HEMO HUMAN)

heparin cofactor 2 P05546 R.LNILNA .F 0.75

(HEP2_HUMAN)

heparin cofactor 2 P05546 R.NFGYTLR.S 0.66

(HEP2_HUM N)

histone deacetylase Q8TEE9 K.LLPPPPIM*SARVLPR.P 0.63 complex subunit (SAP25 HUMAN)

SAP25

hyaluronan-binding Q14520 K.RPGVYTQVT .F 0.68 protein 2 (HABP2_HUMAN)

hyaluronan-binding Q14520 K.FLNWIK.A 0.62 protein 2 (HABP2_HUMAN)

immediate early Q5T953 0.93 response gene 5-like (IER5 L_HUMAN) .MECALDAQSLISISLRKIHSS protein R.T

inactive caspase-12 Q6UXS9 K.AGADTHGRLLQGNICND 0.60

(CASPC HUMAN) AVTK.A

insulin-like growth P35858 K.ANVFVQLPR.L 0.62 factor-binding (ALS_HUMAN)

protein complex

acid labile subunit

inter-alpha-trypsin P19827 K. EL AAQTIKK. S 0.71 inhibitor heavy (ITIH1 HUMAN) Protein description Uniprot ID (name) Peptide S AUC chain HI

inter-alpha-trypsin P19827 K.ILGDM*QPGDYFDLVLFG 0.79 inhibitor heavy (ITIH1 HUMAN) TR.V

chain HI

inter-alpha-trypsin P19827 K.VTFQLTYEEVLKR.N 0.70 inhibitor heavy (ITIH1 HUMAN)

chain HI

inter-alpha-trypsin P19827 R.TMEQFTIHLTVNPQSK.V 0.61 inhibitor heavy (ITIH1 HUMAN)

chain HI

inter-alpha-trypsin P19827 R. F AHYVVT SQ V VNT ANE A 0.63 inhibitor heavy (ITIH1 HUMAN) R.E

chain HI

inter-alpha-trypsin P19823 R. SS ALDMENFRTE VNVLPG 0.89 inhibitor heavy (ITIH2 HUMAN) AK.V

chain H2

inter-alpha-trypsin P19823 K. M QTVE AMK. T 0.93 inhibitor heavy (ITIH2 HUMAN)

chain H2

inter-alpha-trypsin P19823 R.IYLQPGR.L 0.66 inhibitor heavy (ITIH2 HUMAN)

chain H2

inter-alpha-trypsin P19823 K.HLEVDVWVIEPQGLR.F 0.61 inhibitor heavy (ITIH2 HUMAN)

chain H2

inter-alpha-trypsin P19823 K.FYNQVSTPLLR.N 0.89 inhibitor heavy (ITIH2 HUMAN)

chain H2

inter-alpha-trypsin P19823 R.KLGSYEHR.I 0.69 inhibitor heavy (ITIH2 HUMAN)

chain H2

inter-alpha-trypsin Q 14624 K.GSEMVVAGK.L 1.00 Protein description Uniprot ID (name) Peptide S AUC inhibitor heavy (ITIH4 HUMAN)

chain H4

inter-alpha-trypsin Q 14624 R.MNFRPGVLSSR.Q 0.72 inhibitor heavy (ITIH4 HUMAN)

chain H4

inter-alpha-trypsin Q 14624 K.YIFHNFM*ER.L 0.73 inhibitor heavy (ITIH4 HUMAN)

chain H4

inter-alpha-trypsin Q 14624 K.ETLFSVMPGL .M 0.60 inhibitor heavy (ITIH4 HUMAN)

chain H4

inter-alpha-trypsin Q 14624 R.FKPTLSQQQK.S 0.64 inhibitor heavy (ITIH4 HUMAN)

chain H4

inter-alpha-trypsin Q 14624 K.WKETLFSVMPGLK.M 0.69 inhibitor heavy (ITIH4 HUMAN)

chain H4

inter-alpha-trypsin Q 14624 R.RLGVYELLLK.V 0.65 inhibitor heavy (ITIH4 HUMAN)

chain H4

inter-alpha-trypsin Q 14624 R.DTDRFSSHVGGTLGQFYQ 0.69 inhibitor heavy (ITIH4 HUMAN) EVLWGSPAASDDGRR.T

chain H4

inter-alpha-trypsin Q 14624 K.VRPQQLVK.H 0.62 inhibitor heavy (ITIH4 HUMAN)

chain H4

inter-alpha-trypsin Q 14624 R.NVHSAGAAGSR.M 0.69 inhibitor heavy (ITIH4 HUMAN)

chain H4

kallistatin P29622 R.LGFTDLFS .W 0.63

(KAIN HUMAN)

kallistatin P29622 R.VGSALFLSHNLK.F 0.62 Protein description Uniprot ID (name) Peptide S AUC

(KAIN HUMAN)

kininogen-1 P01042 R.VQVVAGKK.Y 0.68

(K G 1 _HUM AN)

leucine-rich alpha- P02750 R. LHLEGNKLQ VLGK. D 0.75 2-glycoprotein (A2GL HUMAN)

lumican P51884 R.FNALQYLR.L 0.77

(LUM HUMAN)

m7GpppX Q96C86 R.IVFENPDPSDGFVLIPDL . 0.94 diphosphatase (DCPS_HUMAN) W

MAGUK p55 Q8N3R9 K. ILEIEDLF S SLK. H 0.69 subfamily member (MPP5_HUMAN)

5

MBT domain- Q05BQ5 K.WFDYLR.E 0.63 containing protein 1 (MBTD 1 HUMAN)

obscurin Q5VST9 R. CELQIRGLAVEDTGEYLC 0.73

(OBSCN HUMAN) VCGQERTSATLTVRA

olfactory receptor Q8NH94 K.DMKQGLAKLM*HR.M 0.89 1L1 (OR 1 L 1 HUMAN)

phosphatidylinositol P80108 K.GIVAAFYSGPSLSDKEK.L 0.79 -glycan-specific (PHLD HUMAN)

phospholipase D

phosphatidylinositol P80108 R.TLLLVGSPTWK.N 0.65 -glycan-specific (PHLD HUMAN)

phospholipase D

phosphatidylinositol P80108 R. WYVP VKDLLGIYEK. L 0.92 -glycan-specific (PHLD HUMAN)

phospholipase D

pigment epithelium- P36955 R.SSTSPTTNVLLSPLSVATA 0.63 derived factor (PEDF HUMAN) LSALSLGAEQR.T

plasma protease C 1 P05155 K.GVTSVSQIFHSPDLAIR.D 0.60 inhibitor (IC1_HUMAN)

PREDICTED: P0C0L4 R.DKGQAGLQRA 0.67 Protein description Uniprot ID (name) Peptide S AUC complement C4-A (C04A HUMAN)

PREDICTED: P0C0L4 K. SH PLNMGK. V 0.87 complement C4-A (C04A HUMAN)

PREDICTED: P0C0L4 R. KKE VYM * P S SIFQDDFVIP 0.67 complement C4-A (C04A HUMAN) DISEPGTW .I

PREDICTED: P0C0L4 R.FGLLDEDGK .T 0.64 complement C4-A (C04A HUMAN)

PREDICTED: P0C0L4 R.KKEVYMPSSIFQDDFVIPD 0.69 complement C4-A (C04A HUMAN) ISEPGTWK.I

PREDICTED: P0C0L4 K. GLC VATPVQLR. V 0.78 complement C4-A (C04A HUMAN)

PREDICTED: P0C0L4 R.YRVFALDQK.M 0.63 complement C4-A (C04A HUMAN)

PREDICTED: P0C0L4 K.AEFQDALEKXNMGITDLQ 0.60 complement C4-A (C04A HUMAN) GLR.L

PREDICTED: P0C0L4 R. EC VGFE AVQE VP VGL VQP 0.60 complement C4-A (C04A HUMAN) AS ATL YD YYNPERR. C

PREDICTED: P0C0L4 K.AEFQDALEKLNMGITDLQ 0.60 complement C4-A (C04A HUMAN) GLR.L

PREDICTED: P0C0L4 R.VTASDPLDTLGSEGALSP 0.61 complement C4-A (C04A HUMAN) GGVASLLR.L

pregnancy zone P20742 R.NELIPLIYLENPRR.N 0.60 protein (PZP HUMAN)

pregnancy zone P20742 K.AVGYLITGYQR.Q 0.67 protein (PZP HUMAN)

protein AMBP P02760 R.AFIQLWAFDAV .G 0.70 preproprotein (AMBP_HUMAN)

protein CBFA2T2 043439 R. LTERE WADE WKHLDHAL 0.61

(MTG8R HUMAN) NCIMEMVE .T protein NLRC3 Q7RTR2 K.ALM*DLLAGKGSQGSQA 0.83

(NLRC3 HUMAN) PQALDR.T

prothrombin P00734 R.TFGSGEADCGLRPLFEK.K 0.69 Protein description Uniprot ID (name) Peptide S AUC preproprotein (THRB_HUMAN)

ras-related GTP- Q7L523 K.ISNIIK.Q 0.68 binding protein A (RRAGA_HUMAN)

retinol-binding P02753 R.FSGTWYAMAK.K 0.64 protein 4 (RET4_HUMAN)

retinol-binding P02753 R. LLNN WD VC ADMVGTFTD 0.61 protein 4 (RET4_HUMAN) TEDPAKFK.M

retinol-binding P02753 K.YWGVASFLQK.G 0.63 protein 4 (RET4_HUMAN)

serum amyloid P- P02743 R. GYVIIKPL V WV . - 0.60 component (SAMP_HUMAN)

sex hormone- P04278 R.LPLVPALDGCLR.R 0.63 binding globulin (SHBG_HUMAN)

spectrin beta chain, Q13813 R.NELIRQEKLEQLAR.R 0.88 non-erythrocytic 1 (SPTN1 HUMAN)

TATA element P82094 K.EELATRLNSSETADLLK.E 0.71 modulatory factor (TMF1 HUMAN)

testicular haploid P0DJG4 R.QCLLNRPFSDNSAR.D 0.67 expressed gene (THEGL HUMAN)

protein-like

thyroxine-binding P05543 K.NALALF VLPK. E 0.61 globulin (THB G HUMAN)

thyroxine-binding P05543 R. SFMLLILER. S 0.64 globulin (THB G HUMAN)

titin Q8WZ42 K.TEPKAPEPISSK.P 0.89

(TITIN HUMAN)

transthyretin P02766 R.GSPAINVAVHVFR.K 0.61

(TTHY_HUMAN)

tripartite motif- Q9C035 R.ELISDLEHRLQGSVM*ELL 0.92 containing protein 5 (TRIM5 HUMAN) QGVDGVIK.R

vitamin D-binding P02774 K. TAMDVF VCT YFMP AAQL 0.88 protein (VTDB_HUMAN) PELPDVELPTNKDVCDPGN Protein description Uniprot ID (name) Peptide S AUC

T .V

vitamin D-binding P02774 K.VM*DKYTFELSR.R 0.70 protein (VTDB_HUMAN)

vitamin D-binding P02774 K. LAQ VPT ADLED VLPL AE 0.61 protein (VTDB_HUMAN) DITNILSK.C

vitamin D-binding P02774 K.SCESNSPFPVHPGTAECCT 0.68 protein (VTDB_HUMAN) K.E

vitamin D-binding P02774 R.KLCMAAL .H 0.71 protein (VTDB_HUMAN)

vitamin D-binding P02774 K.LCDNLSTK.N 0.60 protein (VTDB_HUMAN)

vitamin D-binding P02774 K.VM*DKYTFELSR.R 0.70 protein (VTDB_HUMAN)

vitronectin P04004 R.IYISGM*APR.P 0.75

(VTNC_HUMAN)

vitronectin P04004 R.ERVYFFK.G 0.67

(VTNC_HUMAN)

vitronectin P04004 R.IYISGMAPR.P 0.81

(VTNC_HUMAN)

vitronectin P04004 K.AVRPGYPK.L 0.63

(VTNC HUMAN)

zinc finger protein P52746 K.TRFLLR.T 0.67 142 (ZN142_HUM N)

= Oxidation of methionine

[00165] Table 10. Preeclampsia: Additional peptides significant with AUC > 0.6 by X! Tandem only

Protein description Uniprot ID (name) Peptide XT AUC

(AFAM HUMAN)

alpha- 1 - P01011 Pv. GTHVDLGL AS ANVDFAFSLYK. Q 0.69 antichymotrypsin (AACT_HUMAN)

alpha- 1B- P04217 K.SLPAPWLSM*APVSWITPGLK.T 0.67 glycoprotein (A 1 BG HUMAN)

alpha- 1B- P04217 R. C^ALAPLEGAR.F 0.62 glycoprotein (A 1 BG HUMAN)

alpha-2-antiplasmin P08697 R. WFLLEQPEIQ VAHFPFK.N 0.60

(A2AP HUMAN)

alpha-2-antiplasmin P08697 R.LCQDLGPGAFR.L 0.92

(A2AP HUMAN)

alpha-2-antiplasmin P08697 K.HQMDLVATLSQLGLQELFQAPDL 0.67

(A2AP HUMAN) R.G

alpha-2-HS- P02765 R.QLKEHAVEGDCDFQLLK.L 0.63 glycoprotein (FETUA_HUMAN)

preproprotein

alpha-2-HS- P02765 R. Q^ALKEHAVEGDCDFQLLK.L 0.65 glycoprotein (FETUA_HUMAN)

preproprotein

alpha-2-HS- P02765 K.C^ANLLAEK.Q 0.61 glycoprotein (FETUA_HUMAN)

preproprotein

angiotensinogen P01019 R. SLDFTELDVAAEKIDR.F 0.62 preproprotein (ANGT HUMAN)

angiotensinogen P01019 K.DPTFIPAPIQAK.T 0.78 preproprotein (ANGT HUMAN)

apolipoprotein A-II P02652 K.EPCVESLVSQYFQTVTDYGKDLM 0.67 preproprotein (APOA2 HUMAN) EK.V

apolipoprotein B- P04114 K.FSVPAGIVIPSFQALTAR.F 0.66 100 (APOB HUMAN) Protein description Uniprot ID (name) Peptide XT AUC apolipoprotein B- P04114 K.EQHLFLPFSYK.N 0.90 100 (APOB_HUMAN)

apolipoprotein B- P04114 R. GIIS ALLVPPETEEA . Q 0.70 100 (APOB_HUMAN)

beta-2-glycoprotein P02749 K.C^AFKEHSSLAFW .T 0.70 1 (APOH_HUMAN)

beta-2-glycoprotein P02749 K.EHSSLAFW .T 0.62 1 (APOH HUMAN)

ceruloplasmin P00450 R.FNKNNEGTYYSPNYNPQSR. S 0.64

(CERU_HUMAN)

ceruloplasmin P00450 K.HYYIGIIETTWDYASDHGEK.K 0.63

(CERU_HUMAN)

ceruloplasmin P00450 K.M*YYSAVDPTKDIFTGLIGPM*K.I 0.66

(CERU_HUMAN)

ceruloplasmin P00450 K.M*YYSAVDPTKDIFTGLIGPM*K.I 0.66

(CERU HUMAN)

ceruloplasmin P00450 K.M*YYSAVDPTKDIFTGLIGPMK.I 0.67

(CERU_HUMAN)

ceruloplasmin P00450 K.M*YYSAVDPTKDIFTGLIGPMK.I 0.67

(CERU_HUMAN)

ceruloplasmin P00450 K.MYYSAVDPTKDIFTGLIGPM*K.I 0.67

(CERU_HUMAN)

ceruloplasmin P00450 K.MYYSAVDPTKDIFTGLIGPM*K.I 0.67

(CERU_HUMAN)

ceruloplasmin P00450 R. GVYS SDVFDIFPGTYQTLEM*FPR. 0.67

(CERU_HUMAN) T

coagulation factor P00748 R.VVGGLVALR.G 0.64 XII (FA 12_HUMAN)

complement Clq P02745 K.KGHIYQGSEADSVFSGFLIFPSA.- 0.81 subcomponent (C 1 QA HUMAN)

subunit A

complement Clq P02747 R. Q^ATHQPPAPNSLIR.F 0.64 Protein description Uniprot ID (name) Peptide XT AUC subcomponent (C1QC_HUMAN)

subunit C

complement Cls P09871 R. Q^AFGPYCGHGFPGPLNIETK. S 0.71 subcomponent (C1 S HUMAN)

complement C2 P06681 R.QPYSYDFPEDVAPALGTSFSHML 0.63

(C02 HUMAN) GATNPTQ .T

complement C2 P06681 R.LLGMETMAWQEIR.H 0.70

(C02 HUMAN)

complement C4-B- P0C0L5 R.AVGSGATFSHYYYM*ILSR.G 0.67 like preproprotein (C04B HUMAN)

complement C4-B- P0C0L5 R.FGLLDEDGKKTFFR.G 0.61 like preproprotein (C04B HUMAN)

complement C4-B- P0C0L5 K.ITQVLHFT .D 0.67 like preproprotein (C04B HUMAN)

complement C4-B- P0C0L5 K.M^RPSTDTITVM^VENSHGLR.V 0.65 like preproprotein (C04B HUMAN)

complement C4-B- P0C0L5 K.M*RPSTDTITVM*VENSHGLR.V 0.75 like preproprotein (C04B HUMAN)

complement C5 P01031 R.IVACASYKPSR.E 0.67 preproprotein (C05 HUMAN)

complement C5 P01031 R.SYFPESWLWEVHLVPR.R 0.60 preproprotein (C05_HUMAN)

complement C5 P01031 K.Q^ALPGGQNPVSYVYLEVVS .H 0.74 preproprotein (C05_HUMAN)

complement C5 P01031 K.TLLPVS PEIR.S 0.78 preproprotein (C05 HUMAN)

complement P07358 R.GGASEHITTLAYQELPTADLMQE 0.60 component C8 beta (C08B HUMAN) WGDAVQYNPAII .V

chain preproprotein

complement factor P00751 K.GTDYHKQPWQAK.I 0.89 B preproprotein (CFAB HUMAN)

complement factor P00751 K.VKDISEVVTPR.F 0.64 Protein description Uniprot ID (name) Peptide XT AUC

B preproprotein (CFAB HUMAN)

complement factor P00751 K. Q^A VP AHAPv. D 0.63 B preproprotein (CFAB HUMAN)

complement factor P00751 Pv. GD S GGPLI VHKPv. S 0.79 B preproprotein (CFAB HUMAN)

complement factor P00751 R.FLCTGGVSPYADPNTCR.G 0.71 B preproprotein (CFAB HUMAN)

complement factor P00751 K.KEAGIPEFYDYDVALIK.L 0.74 B preproprotein (CFAB HUMAN)

complement factor P00751 R.YGLVTYATYPK.I 0.88 B preproprotein (CFAB HUMAN)

complement factor P08603 K.EFDHNSNIR.Y 1.00 H (CFAH_HUMAN)

complement factor P08603 K.WSSPPQCEGLPCK.S 0.71 H (CFAH_HUMAN)

complement factor P08603 R. KGE WV ALNPLR. K 0.67 H (CFAH_HUMAN)

complement factor I P05156 K. SLECLHPGT .F 0.60 preproprotein (CFAI_HUMAN)

corticosteroid- P08185 R.GLASANVDFAFSLYK.H 0.62 binding globulin (CBG HUMAN)

fetuin-B Q9UGM5 K.LVVLPFPK.E 0.74

(FETUB HUMAN)

fetuin-B Q9UGM5 R. AS SQ WVVGP S YFVEYLIK. E 0.61

(FETUB HUMAN)

ficolin-3 075636 R.LLGEVDHYQLALGK.F 0.61

(FCN3 HUMAN)

gelsolin P06396 K.QTQVSVLPEGGETPLFK.Q 0.69

(GELS HUMAN)

hemopexin P02790 K.VDGALCMEK.S 0.60

(HEMO HUMAN)

hemopexin P02790 K.SGAQATWTELPWPHEKVDGALC 0.66 Protein description Uniprot ID (name) Peptide XT AUC

(HEMO HUMAN) M*EK.S

hemopexin P02790 K.SGAQATWTELPWPHEKVDGALC 0.66

(HEMO HUMAN) M*EK.S

hemopexin P02790 R. EWF WDL ATGTMK. E 0.68

(HEMO HUMAN)

hemopexin P02790 R. Q^AGHNS VFLIK. G 0.67

(HEMO HUMAN)

heparin cofactor 2 P05546 K.TLEAQLTPR.V 0.67

(HEP2_HUMAN)

histidine-rich P04196 K.DSPVLIDFFEDTER.Y 0.60 glycoprotein (HRG HUMAN)

insulin-like growth P35858 K. ALRDF ALQNP S AVPR. F 0.89 factor-binding (ALS_HUMAN)

protein complex

acid labile subunit

insulin-like growth P35858 R. LWLEGNP WD CGCPLK. A 0.60 factor-binding (ALS_HUMAN)

protein complex

acid labile subunit

inter-alpha-trypsin P19827 K.ILGDM*QPGDYFDLVLFGTR.V 0.85 inhibitor heavy (ITIH1 HUMAN)

chain HI

inter-alpha-trypsin P19823 R.SSALDMENFR.T 0.63 inhibitor heavy (ITIH2 HUMAN)

chain H2

inter-alpha-trypsin P19823 R.SLAPTAAAK.R 0.83 inhibitor heavy (ITIH2 HUMAN)

chain H2

inter-alpha-trypsin P19823 R.LSNENHGIAQR.I 0.76 inhibitor heavy (ITIH2 HUMAN)

chain H2

inter-alpha-trypsin P19823 R.IYGNQDTSSQLKK.F 0.63 Protein description Uniprot ID (name) Peptide XT AUC inhibitor heavy (ITIH2 HUMAN)

chain H2

inter-alpha-trypsin Q 14624 K.TGLLLLSDPDKVTIGLLFWDGR.G 0.60 inhibitor heavy (ITIH4 HUMAN)

chain H4

inter-alpha-trypsin Q 14624 K.YIFHNFM*ER.L 0.70 inhibitor heavy (ITIH4 HUMAN)

chain H4

inter-alpha-trypsin Q 14624 K.IPKPEASFSPR.R 0.65 inhibitor heavy (ITIH4 HUMAN)

chain H4

inter-alpha-trypsin Q 14624 R.QGPVNLLSDPEQGVEVTGQYER. 0.64 inhibitor heavy (ITIH4 HUMAN) E

chain H4

inter-alpha-trypsin Q 14624 R.ANTVQEATFQMELPK.K 0.61 inhibitor heavy (ITIH4 HUMAN)

chain H4

inter-alpha-trypsin Q 14624 K. WKETLF S VMPGLK. M 0.66 inhibitor heavy (ITIH4 HUMAN)

chain H4

inter-alpha-trypsin Q 14624 R.RLDYQEGPPGVEISCWSVEL.- 0.69 inhibitor heavy (ITIH4 HUMAN)

chain H4

inter-alpha-trypsin Q 14624 K. SPEQQETVLDGNLIIR. Y 0.66 inhibitor heavy (ITIH4 HUMAN)

chain H4

kallistatin P29622 K.ALWEKPFISSR.T 0.65

(KAIN HUMAN)

kininogen-1 P01042 R. Q^AVV AGLNFR. I 0.67

(KNG 1 _HUM AN)

kininogen-1 P01042 R. QVVAGLNFR.I 0.71

(KNG 1 _HUM AN) Protein description Uniprot ID (name) Peptide XT AUC kininogen-1 P01042 K.LGQSLDCNAEVYVVPWEK.K 0.62

(K G 1 _HUM AN)

kininogen-1 P01042 R.IASFSQNCDIYPGKDFVQPPTK.I 0.64

(KNG 1 _HUM AN)

leucine-rich alpha- P02750 R. CAGPE AVKGQTLLAV AK. S 0.70 2-glycoprotein (A2GL HUMAN)

leucine-rich alpha- P02750 K.GQTLLAVAK.S 0.67 2-glycoprotein (A2GL HUMAN)

leucine-rich alpha- P02750 K.DLLLPQPDLR.Y 0.71 2-glycoprotein (A2GL HUMAN)

lumican P51884 K.ILGPLSYSK.I 0.83

(LUM HUMAN)

PREDICTED: P0C0L4 R.QGSFQGGFR.S 0.83 complement C4-A (C04A HUMAN)

PREDICTED: P0C0L4 K.YVLPNFEVK.I 0.69 complement C4-A (C04A HUMAN)

PREDICTED: P0C0L4 R.LLATLCSAEVCQCAEGK.C 0.60 complement C4-A (C04A HUMAN)

PREDICTED: P0C0L4 R.VGDTLNLNLR.A 0.66 complement C4-A (C04A HUMAN)

PREDICTED: P0C0L4 R.EPFLSCCQFAESLR.K 0.62 complement C4-A (C04A HUMAN)

PREDICTED: P0C0L4 R.EELVYELNPLDHR.G 0.60 complement C4-A (C04A HUMAN)

PREDICTED: P0C0L4 R.GSFEFPVGDAVSK.V 0.62 complement C4-A (C04A HUMAN)

PREDICTED: P0C0L4 R.GCGEQTMIYLAPTLAASR.Y 0.71 complement C4-A (C04A HUMAN)

pregnancy zone P20742 K.GSFALSFPVESDVAPIAR.M 0.63 protein (PZP HUMAN)

protein AMBP P02760 R.VVAQGVGIPEDSIFTMADRGECV 0.62 preproprotein (AMBP_HUMAN) PGEQEPEPILIPR.V Protein description Uniprot ID (name) Peptide XT AUC prothrombin P00734 R. SGIECQLWR. S 0.65 preproprotein (THRB_HUMAN)

thyroxine-binding P05543 K.MS SI ADFAFNLYR.R 0.63 globulin (THBG HUMAN)

vitronectin P04004 R.MDWLVPATCEPIQSVFFFSGDKY 1.00

(VTNC_HUMAN) YR.V

vitronectin P04004 R.IYISGM*APRPSLAK.K 0.64

(VTNC HUMAN)

vitronectin P04004 R.IYISGMAPRPSLAK.K 0.63

(VTNC_HUMAN)

vitronectin P04004 R.DVWGIEGPIDAAFTR.I 0.61

(VTNC_HUMAN)

zinc finger CCHC Q8N567 R.SCPDNPK.G 0.68 domain-containing (ZCHC9_HUMAN)

protein 9

= Oxidation of Methionine, ^Λ = cyclic pyrolidone derivative by the loss of NH3 (-17 Da)

[00166] Table 11. Candidate peptides and transitions for transferring to the MRM assay

Protein Peptide m/z, fragment ion, m/z, charge, area charge rank

F [y6] - 692.3978+[6] 6193 inter-alpha-trypsin K.VTYDVSR.D 420.216 T[b2]-201.1234+[1] 792556 inhibitor heavy chain HI 5++

ITIH1_HUMAN Y [y5] - 639.3097+[2] 609348

V [y3] - 361.2194+[3] 256946

D[y4]-476.2463+[4] 169546

Y[y5]-320.1585++[5] 110608

S[y2]-262.1510+[6] 50268

D [b4] -479.2136+[7] 13662

Y [b3] - 182.5970++[8] 10947 inter-alpha-trypsin R.EVAFDLEIPK.T 580.813 P [y2]- 244.1656+ [1] 2032509 inhibitor heavy chain HI 5++

ITIH1_HUMAN D [y6] - 714.4032+[2] 672749

A [y8] - 932.5088+[3] 390837

F [y7] - 861.4716+[4] 305087

L [y5] - 599.3763+[5] 255527 inter-alpha-trypsin R.LWAYLTIQELLAK.R 781.453 W[b2]-300.1707+[1] 602601 inhibitor heavy chain HI 1++

ITIH1_HUMAN A[b3]-371.2078+[2] 356967

T[y8]-915.5510+[3] 150419

Y [b4] - 534.2711+[4] 103449

L [b5] - 647.3552+[5] 99820

1 [y7] - 814.5033+[6] 72044

Q [y6] - 701.4192+[7] 66989

E [y5] - 573.3606+[8] 44843 inter-alpha-trypsin K.FYNQVSTPLLR.N 669.364 S [y6] - 686.4196+[1] 367330 inhibitor heavy chain H2 2++

ITIH2_HUMAN V [y7] - 785.4880+[2] 182396

P[y4]-498.3398+[3] 103638

Q [b4] - 553.2405+[4] 54270

Y[b2]-311.1390+[5] 52172

N [b3]-425.1819+[6] 34567 inter-alpha-trypsin K. H LEVD VWVI EPQGL 597.324 P [y5] - 570.3358+[l] 303693 inhibitor heavy chain H2 R.F 7+++

ITIH2_HUMAN 1 [y7] - 812.4625+[2] 206996

E [y6] - 699.3784+[3] 126752

P [y5] - 285.6715++[4] 79841 inter-alpha-trypsin K.TAGLVR.S 308.692 G[y4]-444.2929+[l] 789068 inhibitor heavy chain H2 5++

ITIH2_HUMAN A [b2] - 173.0921+[2] 460019

V[y2]-274.1874+[3] 34333

L [y3] - 387.2714+[4] 29020

G[b3]- 230.1135+[5] 15169 inter-alpha-trypsin R.IYLQPGR.L 423.745 L [y5] - 570.3358+[l] 638209 inhibitor heavy chain H2 2++

ITIH2_HUMAN Y [b2] - 277.1547+[2] 266889

P[y3]-329.1932+[3] 235194

Q [y4] - 457.2518+[4] 171389 inter-alpha-trypsin R.LSNENHGIAQR.I 413.546 N [y9] - 519.7574++[1] 325409 inhibitor heavy chain H2 1+++

ITIH2_HUMAN G [y5] - 544.3202+[2] 139598

S [b2] - 201.1234+[3] 54786

N [y7] - 398.2146++[4] 39521

E [y8] -462.7359++[5] 30623 Protein Peptide m/z, fragment ion, m/z, charge, area charge rank

inter-alpha-trypsin R.SLAPTAAAKR.R 415.242 A [y7] - 629.3617+[1] 582421 inhibitor heavy chain H2 5++

ITIH2 HUMAN P [y6] - 558.3246+ [2] 463815

L[b2]-201.1234+[3] 430584 A [b3] - 272.1605+[4] 204183 T[y5]-461.2718+[5] 47301 pregnancy-specific beta- K.FQLPGQK.L 409.232 L [y5] - 542.3297+[3] 192218 l-glycoprotein 1 0++

PSG1_HUMAN P [y4] -429.2456+[2] 252933

Q[y2]- 275.1714+16] 15366 Q[b2]- 276.1343+[1] 305361 L [b3] - 389.2183+[4] 27279 G [b5] - 543.2926+[5] 18416 pregnancy-specific beta- R.DLYHYITSYVVDGEIII 955.476 G [y7] - 707.3471+[1] 66891 l-glycoprotein 1 YGPAYSGR.E 2+++

PSG1_HUMAN V [y8] - 870.4104+[2] 45076

P [y6] - 650.3257+[3] 28437 1 [y9] - 983.4945+[4] 20423

V [blO] - 628.3033++[5] 17864 E [bl4]-828.3830++[6] 13690 V[bll]-677.8375++[7] 12354 1 [b6] - 805.3879+[8] 11186

V [yl5] -805.4147++[9] 10573 G [bl3] - 763.8617++[10] 10407 pregnancy-specific beta- TLFIFGVTK 513.305 F [y7] -811.4713+[1] 102139 l-glycoprotein 4 1++

PSG4_HUMAN L[b2]-215.1390+[2] 86272

F [y5] - 551.3188+[3] 49520 1 [y6] - 664.4028+[4] 26863 T[y2]-248.1605+[5] 18671 F [b3] - 362.2074+[6] 17343 G [y4] -404.2504+[7] 17122 pregnancy-specific beta- NYTYIWWLNGQSLPV 1097.55 W [b6] - 841.3879+[1] 25756 l-glycoprotein 4 SPR 76++

PSG4_HUMAN G[y9]-940.5211+[2] 25018

Y [b4] - 542.2245+[3] 19778

PSG8_HUMAN LQLSETNR 480.759 T [y3] - 390.2096+[l] 185568

1++

pregnancy-specific beta-l-g lycoprotein 8 Q[b2]-242.1499+[2] 120644

N [y2] - 289.1619+[3] 95164 S [y5] - 606.2842+[4] 84314 L [b3] - 355.2340+[5] 38587 E [y4] - 519.2522+[6] 34807 L [y6] - 719.3682+[7] 17482 E [b5] - 571.3086+[8] 8855 S [b4] - 442.2660+[9] 7070

Pan-PSG ILILPSVTR 506.331 P [y5] - 559.3198+[1] 484395

7++

L[b2]-227.1754+[2] 102774 L[b4]-227.1754++[3] 102774 1 [y7] - 785.4880+[4] 90153 1 [b3] - 340.2595+[5] 45515 L [y6] - 672.4039+[6] 40368 thyroxine-binding K.ELELQIGNALFIGK.H 515.627 E [b3] - 186.5919++[1] 48549 Protein Peptide m/z, fragment ion, m/z, charge, area charge rank

globulin 6+++

THBG_HUMAN E [b3]-372.1765+[2] 28849

G [y2] - 204.1343+[3] 27487

F [bll] - 614.8322++[4] 14892

L[b4]-485.2606+[5] 14552

L[b2]-243.1339+[6] 10169

L[b4]-243.1339++[7] 10169 thyroxine-binding K.AQWANPFDPSK.T 630.804 A [b4] - 457.2194+[1] 48405 globulin 0++

THBG HUMAN S [y2] -234.1448+[2] 43781

D [y4] -446.2245+[3] 26549

D[y4]-446.2245+[4] 25148 thyroxine-binding K.TEDSSSFLIDK.T 621.298 E [b2] - 231.0975+[1] 37113 globulin 4++

THBG HUMAN D[y2]-262.1397+[2] 14495 thyroxine-binding K.AVLHIGEK.G 433.758 V[b2]-171.1128+[l] 151828 globulin 4++

THBG_HUMAN L [y6] - 696.4039+[2] 102903

H [y5] - 583.3198+[3] 73288

1 [_y4] - 446.2609+ [4] 54128

G [y3] - 333.1769+[5] 32717

H [b4]-421.2558+[6] 22662 thyroxine-binding K.AVLHIGEK.G 289.508 L [y6] - 348.7056++[l] 2496283 globulin 0+++

THBG_HUMAN V[b2]-171.1128+[2] 551283

1 [_y4] - 446.2609+ [3] 229168

H [y5] - 292.1636++[4] 212709

H [y5] - 583.3198+[5] 160132

G [y3] - 333.1769+[6] 117961

H [b4]-421.2558+[7] 56579

1 [y4] - 223.6341++[8] 36569

H [b4] - 211.1315++[9] 19460

L[b3]-284.1969+[10] 15758 thyroxine-binding K.FLNDVK.T 368.205 N [y4]-475.2511+[l] 298227 globulin 4++

THBG HUMAN V[y2]-246.1812+[2] 252002

L[b2]-261.1598+[3] 98700

D [y3] - 361.2082+[4] 29215

D[b4]-490.2296+[5] 27258

N [b3] - 375.2027+[6] 10971 thyroxine-binding K.FSISATYDLGATLLK. 800.435 S [b2] - 235.1077+[1] 50075 globulin M 1++

THBG HUMAN G [y6] - 602.3872+[2] 46373

D [y8] -830.4982+ [3] 43372

Y [y9] - 993.5615+[4] 40970

T [y4] - 474.3286+[5] 22161

L [y7] - 715.4713+[6] 19710

S [b4] - 435.2238+[7] 19310

L [y3] - 373.2809+[8] 14157

1 [b3]-348.1918+[9] 13207 thyroxine-binding K.LSNAAHK.A 370.706 H [y2] - 284.1717+[4] 19319 globulin 1++

THBG_HUMAN S [b2] - 201.1234+[1] 60611

N [b3]- 315.1663+[2] 42142 Protein Peptide m/z, fragment ion, m/z, charge, area charge rank

A [b4] - 386.2034+[3] 31081 thyroxine-binding K.GWVDLFVPK.F 530.794 V [y7] -817.4818+[2] 297536 globulin 9++

THBG_HUMAN D [y6] - 718.4134+[4] 226951

L [y5] - 603.3865+[8] 60712

F [y4] - 490.3024+[9] 45586

V [y3] - 343.2340+[6] 134588

P [y2]- 244.1656+ [1] 1619888

V[b3]-343.1765+[7] 126675

D[b4]-458.2034+[10] 14705

F [b6] - 718.3559+[5] 208674

V[b7]-817.4243+[3] 270156 thyroxine-binding K.NALALFVLPK.E 543.339 L[b3]-299.1714+[1] 365040 globulin 5++

THBG_HUMAN P[y2]-244.1656+[2] 274988

A [y7] - 787.5076+[3] 237035

L [y6] - 716.4705+[4] 107838

L [y3] - 357.2496+[5] 103847

L[y8]-900.5917+[6] 97265

F [y5] - 603.3865+[7] 88231

A [b4] - 370.2085+[8] 82559

V[y4]-456.3180+[9] 32352

L [b5] -483.2926+[10] 11974 thyroxine-binding R.SILFLGK.V 389.247 L [y5] - 577.3708+[l] 564222 globulin 1++

THBG_HUMAN 1 [b2]-201.1234+[2] 384240

G [y2] - 204.1343+[3] 302557

L [y3] - 317.2183+[4] 282436

F [y4] - 464.2867+[5] 194047

L [b3] - 314.2074+[6] 27878 leucine-rich alpha-2- R.VLDLTR.N 358.718 D [y4] - 504.2776+[l] 629222 glycoprotein 7++

A2GL_HUMAN L[y5]-617.3617+[2] 236165

L[b2]-213.1598+[3] 171391

L [y3] - 389.2507+[4] 167609

R[yl]- 175.1190+[5] 41213

T[y2]-276.1666+[6] 37194

D[b3]-328.1867+[7] 27029 leucine-rich alpha-2- K.ALGHLDLSGNR.L 576.809 G[y9]-484.7490++[l] 46334 glycoprotein 6++

A2GL_HUMAN L [y7] - 774.4104+[2] 44285

D [y6] - 661.3264+[3] 40188

H [y8]-456.2383++[4] 29392

H [b4] - 379.2088+[5] 26871

L [y5] - 546.2994+[6] 17178

L [b5] -492.2929+[7] 14578 leucine-rich alpha-2- K.LPPGLLANFTLLR.T 712.934 R[yl]- 175.1190+[l] 34435 glycoprotein 8++

A2GL_HUMAN A [b7] - 662.4236+[2] 25768

G [ylO] - 1117.6728+[3] 11662 leucine-rich alpha-2- R.TLDLGENQLETLPPD 1019.04 P [y6] - 710.4196+[1] 232459 glycoprotein LLR.G 68++

A2GL_HUMAN L[y7]-823.5036+[2] 16075

E [y9] - 1053.5939+[3] 15839 Protein Peptide m/z, fragment ion, m/z, charge, area charge rank

D[b3]-330.1660+[4] 15524 leucine-rich alpha-2- R.GPLQLER.L 406.734 P [b2] - 155.0815+[1] 144054 glycoprotein 9++

A2GL_HUMAN Q [y4] - 545.3042+[2] 103146

L [y5] - 658.3883+[3] 77125

L[y3]-417.2456+[4] 65928

R [yl] - 175.1190+[5] 27585

E [y2]-304.1615+[6] 22956 leucine-rich alpha-2- R.LHLEGNK.L 405.727 H [b2] - 251.1503+[1] 79532 glycoprotein 1++

A2GL_HUMAN L [y5] - 560.3039+[2] 54272

G [b5] - 550.2984+[3] 49019

G[y3]-318.1772+[4] 18570

L [b3] - 364.2343+[5] 14068

E [y4]-447.2198+[6] 13318 leucine-rich alpha-2- K.LQVLGK.D 329.218 V[y4]-416.2867+[1] 141056 glycoprotein 3++

A2GL_HUMAN G [y2] - 204.1343+[2] 102478

Q[b2]- 242.1499+13] 98414

L [y3] - 317.2183+[4] 60587

Q [y5] - 544.3453+[5] 50833 leucine-rich alpha-2- K.DLLLPQPDLR.Y 590.340 P [y6] - 725.3941+[1] 592715 glycoprotein 2++

A2GL_HUMAN L [b3] - 342.2023+[2] 570948

L[b2]-229.1183+[3] 403755

P [y6] - 363.2007++[4] 120157

L [y2] - 288.2030+[5] 89508

L [y7] - 838.4781+[6] 76185

L[b4]-455.2864+[7] 60422

L [y7] -419.7427++[8] 45849

P [y4] - 500.2827+ [9] 45223

L [y8] -951.5622+[10] 22393

Q[y5]-628.3413+[11] 15450 leucine-rich alpha-2- R.VAAGAFQGLR.Q 495.280 A[y8]-819.4472+[1] 183637 glycoprotein 0++

A2GL HUMAN G [y7] - 748.4100+[2] 110920

F [y5] - 620.3515+[3] 85535

A [y9] - 890.4843+ [4] 45894

G [y3] - 345.2245+[5] 45644

Q[y4]-473.2831+[6] 40579

A [y8] - 410.2272++[7] 39266

A [b3] - 242.1499+[8] 35890

A [y6] - 691.3886+[9] 29637

G [b4] - 299.1714+[10] 19195

A[b5]-370.2085+[ll] 14944

A [y9] - 445.7458++[12] 11567 leucine-rich alpha-2- R.WLQAQK.D 387.218 L[y5]-587.3511+[1] 80533 glycoprotein 9++

A2GL_HUMAN Q[y4]-474.2671+[2] 57336

A [y3] - 346.2085+ [3] 35952

L[b2]-300.1707+[4] 22509 leucine-rich alpha-2- K.GQTLLAVAK.S 450.779 Q [b2] - 186.0873+[1] 110213 glycoprotein 3++

A2GL_HUMAN T [y7] - 715.4713+[2] 81127 Protein Peptide m/z, fragment ion, m/z, charge, area charge rank

L [y5] - 501.3395+[3] 52292

L [y6] - 614.4236+[4] 46349

A [y4] - 388.2554+ [5] 41283

A [y2] - 218.1499+ [6] 38843

V [y3] - 317.2183+[7] 28961

T [b3] - 287.1350+[8] 23831 leucine-rich alpha-2- R.YLFLNGNK.L 484.763 F [y6] - 692.3726+[l] 61861 glycoprotein 6++

A2GL_HUMAN L[b2]-277.1547+[2] 39468

F [b3] - 424.2231+[3] 21454 L [y5] - 545.3042+[4] 20016 N [y4]-432.2201+[5] 18077 leucine-rich alpha-2- R.NALTGLPPGLFQASA 780.777 T[y8]-902.5557+[l] 44285 glycoprotein TLDTLVLK.E 3+++

A2GL_HUMAN P[yl7]-886.0036++[2] 39557

D [y6] - 688.4240+ [3] 19464 alpha-lB-glycoprotein K. NGVAQEPVH LDSPAI 837.944 P [ylO] - 1076.6099+[1] 130137

K.H 1++

A1BG_HUMAN V[b3]-271.1401+[2] 110650

A [yl3] - 702.8777++[3] 75803 S [y5] - 515.3188+[4] 63197 G[b2]- 172.0717+[5] 57307 E [b6] - 599.2784+[6] 49765 A[b4]-342.1772+[7] 36058 E [yll] - 1205.6525+[8] 34131 P[y4]-428.2867+[9] 31158 H [y8] - 880.4887+[10] 28296 D [y6] - 630.3457+[ll] 20534 L [y7] - 743.4298+[12] 17946 alpha-lB-glycoprotein K.HQFLLTGDTQGR.Y 686.852 Q[b2]-266.1248+[1] 1144372

0++

A1BG_HUMAN F [ylO] - 1107.5793+[2] 725830

T [y7] - 734.3428+[3] 341528 L [y8] - 847.4268+[4] 297048 F [b3]-413.1932+[5] 230163 G [y6] - 633.2951+[6] 226694 T [y4] - 461.2467+[7] 217446 L[y9]-960.5109+[8] 215574 L [b4] - 526.2772+[9] 184306 L [b5] - 639.3613+[10] 157607 Q [yll] - 1235.6379+[ll] 117366 Q[yll]-618.3226++[12] 109274 D[b8]-912.4574+[13] 53233 T [b6] - 740.4090+[14] 49104 D [y5] - 576.2736+[15] 35232 alpha-lB-glycoprotein R.SGLSTGWTQLSK.L 632.830 G [y7] -819.4359+[1] 1138845

2++

A1BG_HUMAN L[b3]-258.1448+[2] 1128060

S [y9] - 1007.5156+[3] 877313 S[y2]-234.1448+[4] 653032 T [y8] - 920.4836+[5] 651216 T [y5] - 576.3352+[6] 538856 W [y6] - 762.4145+[7] 406137 L [y3] - 347.2289+[8] 313255 Protein Peptide m/z, fragment ion, m/z, charge, area charge rank

Q [y4] - 475.2875+[9] 209919

L [ylO] - 560.8035++[10] 103666

W [b7] - 689.3253+[ll] 48587

Q [b9] - 918.4316+[12] 27677

T [b8] - 790.3730+[13] 26742

L [blO] - 1031.5156+[14] 23936 alpha-lB-glycoprotein K.LLELTGPK.S 435.768 E [y6] - 644.3614+[1] 6043967

4++

A1BG_HUMAN L [b2] - 227.1754+[2] 2185138

L [y7] - 757.4454+[3] 1878211 L [y5] - 515.3188+[4] 923148 T [y4] - 402.2347+[5] 699198 G [y3] - 301.1870+[6] 666018 P [y2] - 244.1656+[7] 430183 E [b3] - 356.2180+[8] 244199 alpha-lB-glycoprotein R.GVTFLLR.R 403.250 T [y5] - 649.4032+[l] 4135468

2++

A1BG_HUMAN L [y3] - 401.2871+[2] 2868709

V [b2] - 157.0972+[3] 2109754 F [y4] - 548.3555+[4] 1895653 R [yl] - 175.1190+[5] 918856 L [y2] - 288.2030+[6] 780084 T [b3] - 258.1448+[7] 478494 T [y5] - 325.2052++[8] 415711 F [y4] - 274.6814++[9] 140533 L [b6] - 631.3814+[10] 129473 alpha-lB-glycoprotein K.ELLVPR.S 363.729 P [y2] - 272.1717+[1] 9969478

1++

A1BG_HUMAN L [y4] - 484.3242+[2] 3676023

V [y3] - 371.2401+[3] 2971809 L [b2] - 243.1339+[4] 809753 L [y5] - 597.4083+[5] 159684 alpha-lB-glycoprotein R.SSTSPDR.I 375.174 S [b2] - 175.0713+[1] 89016

8++

A1BG_HUMAN R [yl] - 175.1190+[2] 82740

P [y3] - 387.1987+[3] 76299 T [y5] - 575.2784+[4] 75253 D [b6] - 575.2307+[5] 71180 S [y4] - 474.2307+[6] 53784 alpha-lB-glycoprotein R.LELHVDGPPPRPQLR 862.483 D [b6] - 707.3723+[l] 49322

.A 7++

A1BG_HUMAN G [y9] - 1017.5952+[2] 32049

G [y9] - 509.3012++[3] 27715 alpha-lB-glycoprotein R.LELHVDGPPPRPQLR 575.324 V [yll] - 616.3489++[1] 841163

.A 9+++

A1BG_HUMAN D [ylO] - 566.8147++[2] 621546

E [b2] - 243.1339+[3] 581025 H [yl2] - 684.8784++[4] 485731 R [y5] - 669.4155+[5] 477653 L [yl3] - 741.4204++[6] 369224 H [b4] - 493.2769+[7] 219485 D [b6] - 707.3723+[8] 195842 V [b5] - 592.3453+[9] 170689 R [yl] - 175.1190+[10] 160049 Protein Peptide m/z, fragment ion, m/z, charge, area charge rank

L[b3]-356.2180+[11] 63902

G [b7] - 764.3937+[12] 62128

P [y4] - 513.3144+[13] 33888 alpha-lB-glycoprotein R.ATWSGAVLAGR.D 544.796 S [y8] - 730.4206+[l] 1933290

0++

A1BG_HUMAN G [y7] - 643.3886+[2] 1828931

L [y4] -416.2616+[3] 869412

V [y5] - 515.3300+[4] 615117

A [y3] - 303.1775+[5] 584118

A [y6]- 586.3671+ [6] 471353

W [y9] - 458.7536++[7] 466690

W [y9] - 916.4999+[8] 454934

G [y2] - 232.1404+[9] 338886

S [b4] - 446.2034+[10] 165831

W[b3]-359.1714+[11] 139166

R[yl]- 175.1190+[12] 83145

A [b6] - 574.2620+[13] 65281

G [b5] - 503.2249+[14] 30473

V [b7] - 673.3304+[15] 30408 alpha-lB-glycoprotein R.TPGAAANLELIFVGP 1148.59 G [y9]-999.4755+[l] 39339

QHAGNYR.C 53++

A1BG_HUMAN F [yll] - 1245.6123+[2] 22329

V [ylO] - 1098.5439+[3] 14054

1 [bll] - 1051.5782+[4] 12281

P [y8] -942.4540+[5] 10574 alpha-lB-glycoprotein R.TPGAAANLELIFVGP 766.065 G [y9]-999.4755+[l] 426098

QHAGNYR.C 9+++

A1BG_HUMAN P[y8]-942.4540+[2] 191245

V [ylO] - 1098.5439+[3] 183889

F [yll] - 1245.6123+[4] 172790

G[b3]- 256.1292+[5] 172068

A [y5] - 580.2838+ [6] 170557

A[b4]-327.1663+[7] 146455

H [y6] - 717.3427+[8] 127934

E [b9]-825.4101+[9] 119922

G [y4] - 509.2467+[10] 107378

L[bl0]-938.4942+[ll] 102387

A [b5] - 398.2034+[12] 86428

L[bl0]-469.7507++[13] 68959

E [yl4]-800.9152++[14] 67711

1 [yl2] - 679.8518++[15] 65740

N [b7] - 583.2835+[16] 58648

A [yl7] - 949.9972++[17] 55561

G [y20] - 1049.5451++[18] 51555

1 [bll] - 1051.5782+[19] 51489

L [yl3] - 736.3939++[20] 49190

L[yl5]-857.4572++[21] 48534

A [yl8] - 985.5158++[22] 48337

L [b8] - 696.3675+[23] 47352

N [yl6] -914.4787++[24] 43280

A[b6]-469.2405+[25] 38091

Q [y7] - 845.4013+[26] 32443 insulin-like growth factor- R.SLALGTFAHTPALAS 737.734 G [y6] - 660.3424+[l] 37287 binding protein complex LGLSNNR.L 2+++ Protein Peptide m/z, fragment ion, m/z, charge, area charge rank

acid labile subunit

ALSJH UMAN A [b3] - 272.1605+[2] 21210

S [y8] - 860.4585+[3] 15266

S [y4] - 490.2368+[4] 12497

L [y5] - 603.3209+[5] 9592 insulin-like growth factor- R.ELVLAGNR.L 436.253 A [y4] - 417.2205+[1] 74710 binding protein complex 4++

acid labile subunit

ALSJH UMAN L [y5] - 530.3045+[2] 71602

G [y3] - 346.1833+[3] 39449

V [y6] - 629.3729+[4] 30127 insulin-like growth factor- R.LAYLQPALFSGLAELR 881.498 P [yll] - 1173.6626+[1] 47285 binding protein complex .E 5++

acid labile subunit

ALSJH UMAN Y [b3] - 348.1918+[2] 27425

Q [b5] - 589.3344+[3] 18779

L [b4] - 461.2758+[4] 13442 insulin-like growth factor-binding protein complex 588.001 S [y7] - 745.4203+[l] 29519 acid labile subunit 4+++

ALSJH UMAN A [y4] - 488.2827+[2] 23305

G [y6] - 658.3883+[3] 22089

F [y8] - 892.4887+[4] 16888

Q [b5] - 589.3344+[5] 15807

L [y2] - 288.2030+[6] 15266

Y [b3] - 348.1918+[7] 12835

L [y5] - 601.3668+[8] 12024 insulin-like growth factor- R.ELDLSR.N 366.698 S [y2] - 262.1510+[1] 91447 binding protein complex 0++

acid labile subunit

ALSJH UMAN D [b3] - 358.1609+12] 85115

D [y4] - 490.2620+[3] 75618

L [y3] - 375.2350+[4] 37835 insulin-like growth factor- K.ANVFVQLPR.L 522.303 N [b2] - 186.0873+[1] 90097 binding protein complex 5++

acid labile subunit

ALSJH UMAN F [y6] - 759.4512+[2] 61085

P [y2] - 272.1717+[3] 46657

V [y5] - 612.3828+[4] 43595

V [b3] - 285.1557+[5] 31451

Q [y4] - 513.3144+[6] 28908

V [y7] - 858.5196+[7] 15725

L [y3] - 385.2558+[8] 14324

Q [y4] - 257.1608++[9] 13753 insulin-like growth factor- R.NLIAAVAPGAFLGLK. 727.940 L [b2] - 228.1343+[1] 26729 binding protein complex A 1++

acid labile subunit

ALSJH UMAN 1 [b3] - 341.2183+[2] 25535

P [y8] - 802.4822+[3] 25120

A [y9] - 873.5193+[4] 17542

A [yl2] - 1114.6619+[5] 14895 insulin-like growth factor- R.VAGLLEDTFPGLLGL 835.977 P [y7] - 725.4668+[l] 22005 binding protein complex R.V 4++

acid labile subunit

ALSJH UMAN L [b4] - 341.2183+[2] 13753 Protein Peptide m/z, fragment ion, m/z, charge, area charge rank

E [yll] - 1217.6525+[3] 12611

D [ylO] - 1088.6099+[4] 11003 insulin-like growth factor- R.SFEGLGQLEVLTLDH 833.102 Q [y4] - 503.2824+[l] 328959 binding protein complex NQLQEVK.A 6+++

acid labile subunit

ALS_H UMAN T [yll] - 662.8464++[2] 54479

G [b4] - 421.1718+[3] 24263 insulin-like growth factor- R.NLPEQVFR.G 501.772 P [y6] - 775.4097+[l] 88417 binding protein complex 0++

acid labile subunit

ALS_H UMAN E [y5] - 678.3570+[2] 13620 insulin-like growth factor- R.IRPHTFTGLSGLR.R 485.612 S [y4] - 432.2565+[l] 82619 binding protein complex 4+++

acid labile subunit

ALSJH UMAN L [y5] - 545.3406+[2] 70929

T [b5] - 303.1795++[3] 56677 insulin-like growth factor- K.LEYLLLSR.N 503.800 Y [y6] - 764.4665+[l] 67619 binding protein complex 2++

acid labile subunit

ALSJH UMAN E [b2] - 243.1339+[2] 56261

L [y4] - 488.3191+[3] 32890 L [y5] - 601.4032+[4] 24224 L [y3] - 375.2350+[5] 21139 insulin-like growth factor- R.LAELPADALGPLQR. 732.414 E [b3] - 314.1710+[l] 57859 binding protein complex A 5++

acid labile subunit

ALSJH UMAN P [ylO] - 1037.5738+[2] 45907

P [ylO] - 519.2905++[3] 22723 L [b4] - 427.2551+[4] 14054 insulin-like growth factor- R.LEALPNSLLAPLGR.L 732.432 A [b3] - 314.1710+[1] 52485 binding protein complex 7++

acid labile subunit

ALSJH UMAN P [ylO] - 1037.6102+[2] 37028

E [b2] - 243.1339+[3] 24846 P [ylO] - 519.3087++[4] 15601 p [_y4] - 442.2772+[5] 12327 insulin-like growth factor- R.TFTPQPPGLER.L 621.827 P [y6] - 668.3726+ [1] 57877 binding protein complex 5++

acid labile subunit

ALSJH UMAN P [y8] - 447.2456++[2] 50606 p [b4] - 447.2238+[3] 50606 F [b2] - 249.1234+[4] 42083 P [y8] - 893.4839+[5] 34716 T [y9] - 497.7694++[6] 24220 T [b3] - 350.1710+[7] 22053 insulin-like growth factor- R.DFALQNPSAVPR.F 657.843 A [b3] - 334.1397+[1] 28905 binding protein complex 7++

acid labile subunit

ALSJH UMAN P [y6] - 626.3620+ [2] 23750

P [y2] - 272.1717+[3] 20860 F [b2] - 263.1026+[4] 17536 N [y7] - 740.4050+[5] 15320 Q [y8] - 868.4635+[6] 12525 beta-2-glycoprotein 1 K.FICPLTGLWPINTLK. 886.992 C [b3] - 421.1904+[1] 546451 Protein Peptide m/z, fragment ion, m/z, charge, area charge rank

C 0++

APOH_HUMAN C [yl3] - 756.9158++[2] 438858

P [y6] - 685.4243+[3] 229375

1 [b2] - 261.1598+[4] 188092

W [y7] - 871.5036+[5] 143885

G [y9] - 1041.6091+[6] 143458

T [bl3] - 757.3972++[7] 127058

T [ylO] - 1142.6568+[8] 89126

T [b6] - 732.3749+[9] 51907

L [b5] - 631.3272+[10] 43351

L [b8] - 902.4804+[ll] 38788

N [y4] - 475.2875+[12] 38574

W [b9] - 1088.5597+ [13] 37148

T [y3] - 361.2445+[14] 34153

G [b7] - 789.3964+[15] 22460

P [b4] - 518.2432+[16] 19893

L [y8] - 984.5877+[17] 19180 beta-2-glycoprotein 1 K.FICPLTGLWPINTLK. 591.663 P [y6] - 685.4243+[l] 541745

C 8+++

APOH_HUMAN P [y6] - 343.2158++[2] 234580

G [b7] - 789.3964+[3] 99108

W [y7] - 871.5036+[4] 89126

L [b8] - 902.4804+[5] 68306

C [b3] - 421.1904+[6] 58396

N [y4] - 475.2875+[7] 54474

1 [y5] - 588.3715+[8] 54403

W [y7] - 436.2554++[9] 44706

1 [b2] - 261.1598+[10] 40214

T [y3] - 361.2445+[11] 20535 beta-2-glycoprotein 1 R.VCPFAGILENGAVR. 751.892 P [yl2] - 622.3433++[l] 431648

Y 8++

APOHJHUMAN C [b2] - 260.1063+[2] 223667

P [yl2] - 1243.6793+[3] 134827

G [y9] - 928.5211+[4] 89980

L [y7] - 758.4155+[5] 85773

A [ylO] - 999.5582+[6] 69303

A [b5] - 575.2646+[7] 47913

E [y6] - 645.3315+[8] 44705

N [y5] - 516.2889+[9] 23244

1 [y8] - 871.4996+[10] 20320

G [y4] - 402.2459+[ll] 19180

1 [b7] - 745.3702+[12] 18966

F [b4] - 504.2275+[13] 16399 beta-2-glycoprotein 1 R.VCPFAGILENGAVR. 501.597 E [y6] - 645.3315+[1] 131191

Y 7+++

APOH_HUMAN N [y5] - 516.2889+[2] 130264

1 [b7] - 745.3702+[3] 112154

G [b6] - 632.2861+[4] 102743

G [y4] - 402.2459+[5] 82779

C [b2] - 260.1063+[6] 65453

L [y7] - 758.4155+[7] 54330

1 [b7] - 373.1887++[8] 39143

L [y7] - 379.7114++[9] 29661

V [y2] - 274.1874+[10] 28377 Protein Peptide m/z, fragment ion, m/z, charge, area charge rank

P [yl2] - 622.3433++[ll] 28163 beta-2-glycoprotein 1 K.CTEEGK.W 362.152 E [y3] - 333.1769+[l] 59464

5++

APOH_HUMAN E [b3] - 391.1282+[2] 21675 beta-2-glycoprotein 1 K.WSPELPVCAPIICPPP 940.492 P [yl2] - 648.8692++[l] 294510

SIPTFATLR.V 3+++

APOH_HUMAN P [yll] - 600.3428++[2] 206026

P [y7] - 805.4567+[3] 122891

P [ylO] - 1102.6255+ [4] 75113

L [b5] - 613.2980+[5] 74578

P [yll] - 1199.6783+ [6] 72855

A [b9] - 1040.4870+[7] 28643

T [y3] - 195.1290++[8] 28524

S [b2] - 274.1186+[9] 23770

P [ylO] - 551.8164++[10] 22284

C [yl3] - 728.8845++[ll] 20918

E [b4] - 500.2140+[12] 17114 beta-2-glycoprotein 1 K.ATFGCHDGYSLDGP 796.003 P [y8] - 503.2315++[1] 67031

EEIECTK.L 6+++

APOH_HUMAN E [y4] - 537.2337+[2] 59841

C [b5] - 537.2126+[3] 56454

1 [y5] - 650.3178+[4] 55384

C [y3] - 408.1911+[5] 46946

E [y6] - 779.3604+[6] 45282

T [b2] - 173.0921+[7] 37675

G [y9] - 1062.4772+[8] 36843

C [yl7] - 1005.4144++[9] 35774

P [y8] - 1005.4557+[10] 33991

D [yl0] - 1177.5041+[ll] 30366

E [y7] - 908.4030+[12] 26503

T [y2] - 248.1605+[13] 24840

Y [b9] - 1009.3832+[14] 19491

G [y9] - 531.7422++[15] 17946

S [blO] - 1096.4153+[16] 17352 beta-2-glycoprotein 1 K.ATWYQGER.V 511.766 Y [y5] - 652.3049+[l] 762897

9++

APOH_HUMAN V [y6] - 751.3733+[2] 548908

T [b2] - 173.0921+[3] 252556

V [y7] - 850.4417+[4] 231995

V [b3] - 272.1605+[5] 223140

Q [y4] - 489.2416+[6] 165023

G [y3] - 361.1830+[7] 135013

V [b4] - 371.2289+[8] 86760

V [y7] - 425.7245++[9] 54314 beta-2-glycoprotein 1 K.VSFFCK.N 394.194 S [y5] - 688.3123+[1] 384559

0++

APOH_HUMAN F [y4] - 601.2803+[2] 321951

C [y2] - 307.1435+[3] 265521

S [b2] - 187.1077+[4] 237662

F [y3] - 454.2119+[5] 168104 beta-2-glycoprotein 1 K. CSYTEDAQCI DGTI E 1043.45 P [y2] - 244.1656+ [1] 34574

VPK.C 88++

APOH_HUMAN V [y3] - 343.2340+[2] 9173

E [y4] - 472.2766+[3] 7291 Protein Peptide m/z, fragment ion, m/z, charge, area charge rank

Y[b3]-411.1333+[4] 6233 beta-2-glycoprotein 1 K. CSYTEDAQCI DGTI E 695.975 D [bll] - 672.2476++[l] 37044

VPK.C 0+++

APOH_HUMAN D[y8]-858.4567+[2] 18816

D[b6]-756.2505+[3] 12289

V [y3] - 343.2340+ [4] 11348

A [b7] -414.1474++[5] 9761

G [y7] - 743.4298+[6] 8644 beta-2-glycoprotein 1 K.EHSSLAFWK.T 552.777 H [b2] - 267.1088+[1] 237907

3++

APOH_HUMAN S [y7] - 838.4458+[2] 200568

W[y2]-333.1921+[3] 101078

S [y6] - 751.4137+[4] 54920

A [y4] - 551.2976+[5] 52920

F [y3] - 480.2605+[6] 40102

L [y5] - 664.3817+[7] 30341

F [b7] - 772.3624+[8] 27871

S [b3] - 354.1408+[9] 27754

A [b6] - 625.2940+[10] 25931 beta-2-glycoprotein 1 K.TDASDVKPC- 496.721 D[b2]-217.0819+[1] 323810

3++

APOH_HUMAN P[y2]-276.1013+[2] 119128

A [y7] - 776.3607+[3] 86083

S [y6] - 705.3236+[4] 79262

A [b3] - 288.1190+[5] 77498

D [y5] - 618.2916+[6] 70501

K[y3]-404.1962+[7] 55801

V [y4] - 503.2646+ [8] 46217 transforming growth K.SPYQLVLQHSR.L 443.242 Y[y9]-572.3171++[1] 560916 factor-beta-induced 1+++

protein ig-h3

BGH3_HUMAN P [b2] - 185.0921+[2] 413241

H [y3] - 399.2099+[3] 320572

L [y5] - 640.3525+[4] 313309

Q [y4] - 527.2685+[5] 244398

L[y7]-426.7561++[6] 215854

V [y6] - 739.4209+[7] 172897

L[y7]-852.5050+[8] 164959

Q [y8] - 490.7854++[9] 149814

L [y5] - 320.6799++[10] 127463

L [b5] - 589.2980+[ll] 118061

S[y2]-262.1510+[12] 110123

V [y6] - 370.2141++[13] 97399

P [ylO] - 620.8435++[14] 94640

V [b6] - 688.3665+[15] 87772

Q [b4] - 476.2140+[16] 74203

Y [b3] - 348.1554+[17] 65984

H [y3] - 200.1086++[18] 55624

Q[y4]-264.1379++[19] 41606

L [b7] - 801.4505+[20] 18241

V [b6] - 344.6869++[21] 17678

L[b7]-401.2289++[22] 14976 transforming growth R.VLTDELK.H 409.2 T [y5] - 605.3141+[1] 937957 factor-beta-induced 369++ Protein Peptide m/z, fragment ion, m/z, charge, area charge rank

protein ig-h3

BGH3JHUMAN L [b2] - 213.1598+[2] 298671

L [y6] - 718.3981+[3] 244116

L [y2] - 260.1969+[4] 135739

D [y4] - 504.2664+[5] 52472

E [y3] - 389.2395+[6] 50839 transforming growth K.VISTITNNIQQIIEIED 897.4 E [y8] - 1010.4789+[1] 282865 factor-beta-induced TFETLR.A 798+++

protein ig-h3

BGH3_HUMAN D [y7] - 881.4363+[2] 237234

1 [y9] - 1123.5630+[3] 195581

T [y6] - 766.4094+[4] 186875

1 [b2] - 213.1598+[5] 174492

T [y3] - 389.2507+[6] 145598

F [y5] - 665.3617+[7] 143872

E [y4] - 518.2933+[8] 108148

Q [bll] - 606.8328++[9] 106647

1 [b5] - 514.3235+[10] 82030

N [b8] - 843.4571+[ll] 75125

T [b4] - 401.2395+[12] 71448

1 [bl2] - 663.3748++[13] 58314

N [b7] - 365.2107++[14] 54862

1 [b9] - 956.5411+[15] 51034

L [y2] - 288.2030+[16] 50734

S [b3] - 300.1918+[17] 48708

Q [blO] - 542.8035++[18] 43754

Q [bll] - 1212.6583+[19] 37375

T [b6] - 615.3712+[20] 33322

1 [b9] - 478.7742++[21] 29570

Q [blO] - 1084.5997+[22] 25817

T [y6] - 383.7083++[23] 17187

N [b8] - 422.2322++[24] 17111

1 [bl3] - 719.9168++[25] 16661 transforming growth K.IPSETLNR.I 465.2 S [y6] - 719.3682+[1] 326570 factor-beta-induced 562++

protein ig-h3

BGH3_HUMAN P [y7] - 816.4210+[2] 168951

E [y5] - 632.3362+[3] 102452

P [b2] - 211.1441+[4] 85885

T [y4] - 503.2936+[5] 67650

L [y3] - 402.2459+[6] 20939

N [y2] - 289.1619+[7] 13979 transforming growth R.ILGDPEALR.D 492.2 P [y5] - 585.3355+[l] 1431619 factor-beta-induced 796++

protein ig-h3

BGH3_HUMAN G [y7] - 757.3839+[2] 1066060

L [b2] - 227.1754+[3] 742225

L [y8] - 870.4680+[4] 254257

D [b4] - 399.2238+[5] 159932

G [b3] - 284.1969+16] 66816

D [y6] - 700.3624+[7] 65780

A [y3] - 359.2401+[8] 62730

E [y4] - 488.2827+[9] 23711

L [y2] - 288.2030+[10] 16344 Protein Peptide m/z, fragment ion, m/z, charge, area charge rank

transforming growth R.DLLNNHILK.S 360.5 L[y7]-426.2585++[l] 1488651 factor-beta-induced 451+++

protein ig-h3

BGH3_HUMAN L[b2]-229.1183+[2] 591961

N [y6] - 369.7165++[3] 366710

N [y5] - 624.3828+[4] 103993

L[y2]-260.1969+[5] 75103

N [b4] - 228.6263++[6] 66125

N [y6] - 738.4257+[7] 49493

H [y4] - 510.3398+[8] 43681

N [y5] - 312.6950++[9] 41551

1 [y3] - 373.2809+[10] 40285

L [b3] - 342.2023+[ll] 33494

L[y8]-482.8006++[12] 33034 transforming growth K.AIISNK.D 323.2 1 [y4]-461.2718+[l] 99850 factor-beta-induced 001++

protein ig-h3

BGH3_HUMAN 1 [b2] - 185.1285+[2] 43105

S[y3]-348.1878+[3] 39192

N [y2]-261.1557+[4] 24516 transforming growth K.DILATNGVIHYIDELLI 804.1 P[y5]-517.2617+[1] 400251 factor-beta-induced PDSAK.T 003+++

protein ig-h3

BGH3_HUMAN 1 [b2]-229.1183+[2] 306709

L [b3] - 342.2023+[3] 147923

1 [y6] - 630.3457+14] 91265

S [y3] - 305.1819+[5] 61472

L [y7] - 743.4298+[6] 57894

A[b4]-413.2395+[7] 52430

H [yl3] - 757.3985++[8] 30183

G[yl6]-891.9855++[9] 27711

D [ylO] - 1100.5834+[10] 24979

A [yl9] - 1035.0493++[11] 23223

L [y8] -856.5138+[12] 22507

L [y20] - 1091.5913++[13] 16783 transforming growth K.TLFELAAESDVSTAID 1049. D [y4] - 550.2984+[l] 64464 factor-beta-induced LFR.Q 5388++

protein ig-h3

BGH3_HUMAN S [y8] - 922.4993+[2] 47291

S [yll] - 1223.6266+[3] 44234

A[b6]-675.3712+[4] 35972

L [b5] - 604.3341+[5] 34997

A [b7] - 746.4083+[6] 33045

E [b4]-491.2500+[7] 31744

D [ylO] - 1136.5946+[8] 30183

E [b8]-875.4509+[9] 26475

F[y2]-322.1874+[10] 25044

T[y7]-835.4672+[ll] 21596

1 [y5] - 663.3824+[12] 21011

L [y3] -435.2714+[13] 20295

L[b2]-215.1390+[14] 20295

V [y9] - 1021.5677+[15] 18929

A [y6] - 734.4196+[16] 17694

F [b3] - 362.2074+[17] 14441 Protein Peptide m/z, fragment ion, m/z, charge, area charge rank

transforming growth R.QAGLGNHLSGSER.L 442.5 G[y9]-478.7309++[l] 180677 factor-beta-induced 567+++

protein ig-h3

BGH3_HUMAN L [ylO] - 535.2729++[2] 147807

S [y5] - 535.2471+[3] 129825

G [yll] - 563.7836++[4] 84584

L [y6] - 648.3311+[5] 51642

A [b2] - 200.1030+[6] 26469

G[y4]-448.2150+[7] 26397

H [y7] - 393.1987++[8] 25390

A [yl2] - 599.3022++[9] 21434

N [y8]-450.2201++[10] 19276 transforming growth R.LTLLAPLNSVFK.D 658.4 P[y7]-804.4614+[1] 1635673 factor-beta-induced 028++

protein ig-h3

BGH3JHUMAN A[y8]-875.4985+[2] 869779

L [b3] - 328.2231+[3] 516429

T [b2] - 215.1390+[4] 415472

L[y9]-988.5826+[5] 334225

L [b4] -441.3071+[6] 209200

L [ylO] - 1101.6667+[7] 174268

A[b5]-512.3443+[8] 160217

A [y8] - 438.2529++ [9] 83264

N [y5] - 594.3246+[10] 54512

F[y2]-294.1812+[11] 51649

L[y9]-494.7949++[12] 34541

L [y6] - 707.4087+[13] 34086

S [y4] - 480.2817+[14] 30053

T [yll] - 1202.7143+[15] 16653 transforming growth K.DGTPPIDAHTR.N 393.8 P[y8]-453.7432++[l] 355240 factor-beta-induced 633+++

protein ig-h3

BGH3_HUMAN P[y7]-405.2169++[2] 88181

T [b3] - 274.1034+[3] 81204

G [b2] - 173.0557+[4] 40062

D [y5] - 599.2896+[5] 37689

A [y4] - 242.6350++[6] 29633

P [y7] -809.4264+[7] 22153

1 [y6] - 712.3737+[8] 16327 transforming growth K.YLYHGQTLETLGGK. 527.2 E [y6] - 604.3301+[1] 483222 factor-beta-induced K 753+++

protein ig-h3

BGH3_HUMAN Y [yl2] - 652.3357++[2] 264640

T [y5] - 475.2875+[3] 239600

G[y3]-261.1557+[4] 206272

L[b2]-277.1547+[5] 134992

L [yl3] - 708.8777++[6] 119379

T[b7]-863.4046+[7] 104307

L [y4] - 374.2398+[8] 100344

H [yll] - 570.8040++[9] 93318

L [y7] - 717.4141+[10] 91276

G [bl3] - 717.3566++[11] 80707

T[y8]-818.4618+[12] 57888

Q[b6]- 762.3570+[13] 54766 Protein Peptide m/z, fragment ion, m/z, charge, area charge rank

G [ylO] - 1003.5419+[14] 51523

T[b7]-432.2060++[15] 49121

G [y2] - 204.1343+[16] 45518

T[y8]-409.7345++[17] 44437

L [y7] - 359.2107++[18] 33028

T [blO] - 603.7931++[19] 26902

G [b5] - 634.2984+[20] 21858

Q[b6]- 381.6821++[21] 17595

H [b4] - 577.2769+[22] 16093

L[b8]-488.7480++[23] 15133

T[y5]-238.1474++[24] 15013

E [b9] - 553.2693++[25] 12370 transforming growth R.EGVYTVFAPTNEAFR 850.9 P[y7]-834.4104+[1] 364143 factor-beta-induced .A 176++

protein ig-h3

BGH3JHUMAN F [y9] - 1052.5160+[2] 269144

A [y8] - 905.4476+ [3] 176007

V[b3]-286.1397+[4] 107490

V [ylO] - 1151.5844+[5] 74822

T [b5] - 550.2508+[6] 47560

V [b6] - 649.3192+[7] 45398

G [b2] - 187.0713+[8] 43056

Y[b4]-449.2031+[9] 33148

F [b7] - 796.3876+[10] 24440

A[b8]-867.4247+[ll] 24020

E [y4] - 522.2671+[12] 17174

A [y3] - 393.2245+[13] 14712

F[y2]-322.1874+[14] 12611 transforming growth R.LLGDAK.E 308.6 A[y2]-218.1499+[1] 206606 factor-beta-induced 869++

protein ig-h3

BGH3JHUMAN G [y4] - 390.1983+[2] 204445

L [y5] - 503.2824+[3] 117829

L[b2]-227.1754+[4] 43998 transforming growth K.ELANILK.Y 400.7 A [y5] - 558.3610+[1] 963502 factor-beta-induced 475++

protein ig-h3

BGH3_HUMAN L[y2]-260.1969+[2] 583986

N [y4]-487.3239+[3] 326252

1 [y3] - 373.2809+[4] 302352

1 [b5] - 541.2980+[5] 179670

L[b2]-243.1339+[6] 74642

L [y6] - 671.4450+[7] 38792

N [b4] - 428.2140+[8] 14952 transforming growth K.YHIGDEILVSGGIGAL 935.0 H [b2]-301.1295+[l] 24601 factor-beta-induced VR.L 151++

protein ig-h3

BGH3_HUMAN S [y9] - 829.4890+[2] 15456 transforming growth K.YHIGDEILVSGGIGAL 623.6 S[y9]-829.4890+[l] 917445 factor-beta-induced VR.L 791+++

protein ig-h3

BGH3_HUMAN G [y5] - 515.3300+[2] 654048

1 [b7] - 828.3886+[3] 553713

G [y8] - 742.4570+[4] 467481 Protein Peptide m/z, fragment ion, m/z, charge, area charge rank

L[b8]-941.4727+[5] 322194

G [y7] - 685.4355+[6] 228428

E [b6] - 715.3046+[7] 199383

V [ylO] - 928.5574+[8] 141616

G[b4]-471.2350+[9] 126224

L[b8]-471.2400++[10] 117080

H [b2]-301.1295+[ll] 107162

1 [y6] - 628.4141+[12] 105488

A [y4] - 458.3085+[13] 103491

L [y3] - 387.2714+[14] 73094

1 [b3] -414.2136+[15] 72515

S [y9] -415.2482++[16] 65044

V [b9] - 1040.5411+[17] 61760

V[y2]-274.1874+[19] 56093

1 [b7] - 414.6980++[18] 56093

V [b9] - 520.7742++[20] 39413

L[yll]-1041.6415+[21] 38962

D [b5] - 586.2620+[22] 36257

S [blO] - 564.2902++[23] 32329

1 [y6] - 314.7107++[24] 30526

A [bl5] - 741.8830++[25] 27692

V [y 10] -464.7824++ [26] 26340

L[yll]-521.3244++[27] 20415

G [bl2] - 621.3117++[28] 18612

G [bl2] - 1241.6161+[29] 13073 transforming growth K.LEVSLK.N 344.7 V[y4]-446.2973+[l] 120860 factor-beta-induced 156++

protein ig-h3

BGH3_HUMAN E [y5] - 575.3399+[2] 82786

E [b2]-243.1339+[3] 76794

S [y3] - 347.2289+[4] 36335

L[y2]-260.1969+[5] 24932 transforming growth K.NNVVSVNK.E 437.2 V [y5] - 546.3246+[l] 17073 factor-beta-induced 431++

protein ig-h3

BGH3_HUMAN N [b2] - 229.0931+[2] 14045 transforming growth R.GDELADSALEIFK.Q 704.3 E [b3] - 302.0983+[l] 687754 factor-beta-induced 537++

protein ig-h3

BGH3_HUMAN A[y9]-993.5251+[2] 431716

D [y8] -922.4880+[3] 368670

D [b2] - 173.0557+[4] 358545

F [y2] - 294.1812+[5] 200930

L[b4]-415.1823+[6] 197364

S [y7] - 807.4611+[7] 187412

1 [y3] - 407.2653+[8] 129601

A[b5]-486.2195+[9] 121605

E [y4] - 536.3079+[10] 108432

A[y6]-720.4291+[11] 107627

L [y5] - 649.3919+[12] 95662

L [ylO] - 1106.6092+[13] 79325

D [b6] - 601.2464+[14] 42625

A[b8]-759.3155+[15] 28647

S [b7] - 688.2784+[16] 20709 Protein Peptide m/z, fragment ion, m/z, charge, area charge rank

transforming growth K.QASAFSR.A 383.6 F [y3] -409.2194+[1] 64604 factor-beta-induced 958++

protein ig-h3

BGH3_HUMAN S [y5] - 567.2885+[2] 60496

S[y2]-262.1510+[3] 42825

A [y4] - 480.2565+ [4] 25211 transforming growth R.LAPVYQK.L 409.7 P [y5] - 634.3559+ [1] 416225 factor-beta-induced 422++

protein ig-h3

BGH3JHUMAN Y [y3] - 438.2347+[2] 171715

V [y4] - 537.3031+[3] 98187

Q[y2]- 275.1714+14] 42056

A [y6] - 705.3930+[5] 32429 ceruloplasmin K.USVDTEHSNIYLQNG 724.362 1 [b2] - 227.1754+[1] 168111

PDR.I 4+++

CERU_HUMAN N [y5] - 558.2630+[2] 87133

G[y4]-444.2201+[3] 86682

L [y7] - 799.4057+[4] 84956

Q [y6] - 686.3216+[5] 79928

Y [y8] - 962.4690+[6] 64167

S [b3] - 314.2074+[7] 39476

N [ylO] - 1189.5960+[8] 24691

P[y3]-387.1987+[9] 22065

1 [yl8] - 1029.4980++[10] 20714

N [blO] - 1096.5269+[11] 18087

1 [y9] - 1075.5531+[12] 15460 ceruloplasmin K.ALYLQYTDETFR.T 760.375 Y [b3] - 348.1918+[1] 681082

0++

CERU_HUMAN Y[y7]-931.4156+[2] 405797

Q [y8] - 1059.4742+[3] 343430

T [y6] - 768.3523+[4] 279638

L [b2] - 185.1285+[5] 229654

L [y9] - 1172.5582+[6] 164660

L [b4] -461.2758+[7] 142145

D [y5] - 667.3046+[8] 107547

Y [ylO] - 668.3144++[9] 91862

E [y4] - 552.2776+[10] 76852

Q[b5]- 589.3344+[ll] 75200

T[y3]-423.2350+[12] 64168

F[y2]-322.1874+[13] 47807

Y [b6] - 752.3978+[14] 40377

L [y9] - 586.7828++[15] 40227 ceruloplasmin R.TTIEKPVWLGFLGPII 956.569 E [b4]-445.2293+[l] 92012

K.A 0++

CERU_HUMAN K [b5] - 573.3243+[2] 45856

L [y9] -957.6132+[3] 32272

G[y8]-844.5291+[4] 29044

K [yl3] - 734.4579++[5] 26118

G [y5] - 527.3552+[6] 24917

L [y6] - 640.4392+[7] 19738

1 [b3]-316.1867+[8] 18838

P[y4]-470.3337+[9] 18012

W [ylO] - 1143.6925+[10] 17412

1 [yl5]-855.5213++[ll] 14785 Protein Peptide m/z, fragment ion, m/z, charge, area charge rank

V [b7] - 769.4454+[12] 14710 ceruloplasmin R.TTIEKPVWLGFLGPII 638.048 G [y8] -844.5291+[1] 1645779

K.A 4+++

CERU_HUMAN G [y5] - 527.3552+[2] 1180842

L [y6] - 640.4392+[3] 920117

T [b2] - 203.1026+[4] 775570

F [y7] - 787.5076+[5] 416229

P[y4]-470.3337+[6] 285341

W [b8] -955.5247+[7] 275960

1 [y2]-260.1969+[8] 256597

V [b7] - 769.4454+[9] 230104

E [b4]-445.2293+[10] 117754

W[b8]-478.2660++[ll] 105521

P [yl2] - 670.4105++[13] 104020

P [b6] - 670.3770+[12] 104020

G [blO] - 1125.6303+[14] 93363

F [y7] - 394.2575++[15] 76176

K [b5] - 573.3243+[16] 63718

1 [b3]-316.1867+[17] 52986

L [b9] - 1068.6088+ [18] 33548

1 [y3] - 373.2809+[19] 20864 ceruloplasmin K.VYVHLK.N 379.731 V[y4]-496.3242+[l] 228979

6++

CERU_HUMAN Y [y5] - 659.3875+[2] 196857

H [y3] - 397.2558+[3] 89610

Y [b2] - 263.1390+[4] 88034

L[y2]-260.1969+[5] 85482

Y[y5]-330.1974++[6] 31821 ceruloplasmin R.IYHSHIDAPK.D 590.809 H [y8]-452.7354++[l] 167209

1++

CERU_HUMAN P[y2]-244.1656+[2] 84831

A [y3] - 315.2027+[3] 78036

S [y7] - 767.4046+[4] 75864

H [b3]-414.2136+[5] 67808

Y [y9] - 534.2671++[6] 50296

H [y8] - 904.4635+[7] 42801

D[b7]-866.4155+[8] 28721

H [y6] - 680.3726+[9] 23817

A[b8]-937.4526+[10] 19964

D[y4]-430.2296+[ll] 17653

Y [b2] - 277.1547+[12] 16742 ceruloplasmin R.IYHSHIDAPK.D 394.208 H [y8]-452.7354++[l] 402227

5+++

CERU_HUMAN Y [y9] - 534.2671++[2] 305348

P[y2]-244.1656+[5] 101993

A [y3] - 315.2027+[3] 97580

Y [b2] - 277.1547+[4] 93377

D [y4] -430.2296+[6] 89734

S [y7] - 767.4046+[7] 88263

S [y7] - 384.2060++[8] 60663

1 [y5] - 543.3137+[9] 44692

H [y6] - 680.3726+[ll] 38528

A [b8] - 469.2300++[10] 37547

H [b5] - 638.3045+[12] 36146 Protein Peptide m/z, fragment ion, m/z, charge, area charge rank

H [b3]-414.2136+[13] 23467 ceruloplasmin R.HYYIAAEEIIWNYAPS 905.454 P [y9] -977.5302+[l] 253794

GIDIFTK.E 9+++

CERU_HUMAN E [b8]-977.4363+[2] 233479

Y[b2]-301.1295+[3] 128823

1 [b9] - 1090.5204+[4] 103955

A [ylO] - 1048.5673+[5] 78247

P[y9]-489.2687++[6] 76005

E [b8] -489.2218++[7] 76005

1 [blO] - 1203.6045+[8] 56671

F [y3] - 395.2289+[9] 49456

Y[b3]-464.1928+[10] 46864

E [b7]-848.3937+[ll] 44622

A [b5] - 648.3140+[12] 42451

A[b6]-719.3511+[13] 40629

1 [b4] - 577.2769+[14] 39999

D [y5] - 623.3399+[15] 29631

1 [y4] - 508.3130+ [16] 28581

T[y2]-248.1605+[17] 27040

1 [blO] - 602.3059++[18] 24448

Y[yll]- 1211.6307+[19] 24238

G [y7] - 793.4454+[20] 21926

W [bll] - 695.3455++[21] 18704

S [y8] - 880.4775+[22] 18633 ceruloplasmin R.IGGSYK.K 312.671 G[y5]-511.2511+[1] 592392

2++

CERU_HUMAN G [y4] -454.2296+[2] 89266

G[b2]-171.1128+[3] 71261

Y[y2]-310.1761+[4] 52498

S [y3] - 397.2082+[5] 22364 ceruloplasmin R.EYTDASFTNR.K 602.267 S [y5] - 624.3100+[1] 163623

5++

CERU HUMAN F [y4] - 537.2780+[2] 83580

T[y8]-911.4217+[3] 83391

A[y6]-695.3471+[4] 82886

D[y7]-810.3741+[5] 76315

T [y3] - 390.2096+[6] 66018

Y[b2]-293.1132+[7] 50224

N [y2] - 289.1619+[8] 29376 ceruloplasmin R.GPEEEHLGILGPVIW 829.767 A[y8]-860.4472+[l] 259776

AEVGDTIR.V 5+++

CERU_HUMAN W [y9] - 1046.5265+[2] 210032

E [y7] - 789.4101+[3] 201448

G [y5] - 561.2991+[4] 189809

V [y6] - 660.3675+[5] 121142

T [y3] - 389.2507+[6] 80306

P [b2] - 155.0815+[7] 65806

V [bl3] - 664.8459++[8] 65676

G [bll] - 1132.5633+[9] 64765

1 [ylO] - 1159.6106+[10] 58783

L [blO] - 1075.5419+[11] 56702

1 [b9] - 962.4578+[12] 54101

L[b7]-792.3523+[13] 48509

P[bl2]-615.3117++[14] 37715 Protein Peptide m/z, fragment ion, m/z, charge, area charge rank

D [y4] - 504.2776+[15] 34528

G[b8]-849.3737+[16] 34008

1 [bl4] - 721.3879++[17] 23669

H [b6] - 679.2682+[18] 22174

W [bl5] - 814.4276++[19] 21979

E [b3]-284.1241+[20] 18272

G[bll]-566.7853++[21] 17882

A [bl6] - 849.9461++[22] 15476 ceruloplasmin R.VTFHNK.G 373.203 T [y5] - 646.3307+[l] 178952

2++

CERU_HUMAN F [y4] - 545.2831+[2] 175829

T [b2] - 201.1234+[3] 127758

N [y2]-261.1557+[4] 107852

H [y3] - 398.2146+[5] 103754 ceruloplasmin K.GAYPLSI EPIGVR. F 686.385 S [y8] -870.5043+[l] 970541

2++

CERU HUMAN P [y5] - 541.3457+[2] 966508

P [ylO] - 1080.6412+[3] 590391

E [y6] - 670.3883+[4] 493076

1 [y7] - 783.4723+[5] 391013

Y [b3] - 292.1292+[6] 265598

L[y9]-983.5884+[7] 217591

P[b4]-389.1819+[8] 188839

S [b6] - 589.2980+[9] 95623

G [y3] - 331.2088+[10] 85605

L [b5] - 502.2660+[ll] 76628

V[y2]-274.1874+[12] 52365

1 [b7] - 702.3821+[13] 39225

E [b8]-831.4247+[14] 26866 ceruloplasmin K.NNEGTYYSPNYNPQ 952.413 P [y4] -487.2623+[l] 37339

SR.S 9++

CERU_HUMAN S [y9] - 1062.4963+[2] 33696

P[y8]-975.4643+[3] 29467

N [y5] - 601.3052+[4] 24068

N [b2] - 229.0931+[5] 19060

Y [ylO] - 1225.5596+[6] 16718

E [b3]-358.1357+[7] 16523 ceruloplasmin R.SVPPSASHVAPTETF 844.419 P [y2]- 244.1656+ [1] 579331

TYEWTVPK.E 9+++

CERU HUMAN T [y8] - 1023.5146+[2] 126817

W [y5] - 630.3610+[3] 101524

V [y3] - 343.2340+ [4] 99970

Y [y7] - 922.4669+[5] 95448

E [y6] - 759.4036+[6] 88030

T [y4] - 444.2817+[7] 55884

F [y9] - 1170.5830+[8] 55743

V[b2]- 187.1077+[9] 46982

P [y20] - 1124.5497++[10] 37303

P[b3]-284.1605+[11] 21690

E [bl8] -951.4494++ [12] 18652

P [b4] - 381.2132+[13] 16956

T [bl4] - 681.3384++[14] 15543 ceruloplasmin K.GSLHANGR.Q 271.143 L[y6]-334.1854++[1] 154779

8+++ Protein Peptide m/z, fragment ion, m/z, charge, area charge rank

CERU_HUMAN A[y4]-417.2205+[2] 41628

S [y7] - 377.7014++[3] 35762 H [y5] - 277.6433++[4] 29542 ceruloplasmin R.QSEDSTFYLGER.T 716.323 G[y3]-361.1830+[1] 157040

0++

CERU_HUMAN Y [y5] - 637.3304+[2] 126155

F [y6] - 784.3988+[3] 97814 L[y4]-474.2671+[4] 80146 T[y7]-443.2269++[5] 70746 T [y7] - 885.4465+[6] 54844 S [y8] - 972.4785+[7] 44101 S [b2] - 216.0979+[8] 42193 D [y9] - 1087.5055+ [9] 36186 E [ylO] - 1216.5481+[10] 35055 E [b3]-345.1405+[ll] 20778 E [y2]-304.1615+[12] 19153 ceruloplasmin R.TYYIAAVEVEWDYSP 1045.49 P [y3]- 400.2303+ [1] 64887

QR.E 69++

CERU_HUMAN Y[b3]-428.1816+[2] 49716

S [y4] - 487.2623+[3] 37369

Y [b2] - 265.1183+[4] 35596 E [y8] - 1080.4745+[5] 28569 W[y7]- 951.4319+[6] 26204

V [b7] - 782.4083+[7] 23577 A [b6] - 683.3399+[8] 23512

V [y9] - 1179.5429+[10] 22526 D [y6] - 765.3526+[9] 22526

Y [y5] - 650.3257+[ll] 19965 A[b5]-612.3028+[12] 18520 ceruloplasmin K.ELHHLQEQNVSNAF 674.672 N [y6] - 707.3723+[l] 22715

LDK.G 8+++

CERU_HUMAN L [y3] - 188.1155++[2] 21336

S [y7] - 794.4043+[3] 10176 ceruloplasmin K.GEFYIGSK.Y 450.726 E [b2] - 187.0713+[1] 53262

7++

CERU_HUMAN F [y6] - 714.3821+[2] 50438

1 [y4] - 404.2504+ [3] 39602 Y [y5] - 567.3137+[4] 34020 G[y3]-291.1663+[5] 33100 ceruloplasmin R.QYTDSTFR.V 509.235 T [y6] - 726.3417+[1] 164056

4++

CERU_HUMAN S [y4] - 510.2671+[2] 155584

D [y5] - 625.2940+[3] 136472 T [y3] - 423.2350+[4] 54313 F[y2]-322.1874+[5] 47220

Y [b2] - 292.1292+[6] 27846

Y [y7] - 889.4050+[7] 16550 ceruloplasmin K.AEEEHLGILGPQLHA 710.027 E [b2] - 201.0870+[1] 60743

DVGDK.V 2+++

CERU_HUMAN V[y4]-418.2296+[2] 23296

E [yl7] -899.9759++[3] 14619 ceruloplasmin K.LEFALLFLVFDENES 945.137 L[y6]-359.1925++[1] 19544

WYLDDNIK.T 2+++

CERU_HUMAN L [b5] - 574.3235+[2] 17902 Protein Peptide m/z, fragment ion, m/z, charge, area charge rank

ceruloplasmin K.TYSDHPEK.V 488.722 S [y6] - 712.3260+[1] 93810

2++

CERU HUMAN P [y3] - 373.2082+[2] 43778

Y[b2]-265.1183+[3] 35960

H [y4] - 510.2671+[4] 16651 ceruloplasmin K.TYSDHPEK.V 326.150 S [y6] - 356.6667++[l] 539251

5+++

CERU_HUMAN Y[y7]-438.1983++[2] 180506

Y[b2]-265.1183+[3] 109445

P [y3] - 373.2082+[4] 84742

H [y4] - 255.6372++[5] 27596

P[y3]- 187.1077++[6] 25016

D [y5] - 625.2940+[7] 24000

H [y4] - 510.2671+[8] 20795 hepatocyte growth factor R.YEYLEGGDR.W 551.246 E [b2]-293.1132+[l] 229354 activator 0++

HGFA_HUMAN Y [y7] - 809.3788+[2] 204587

L [y6] - 646.3155+[3] 96740

Y[b3]-456.1765+[4] 54186

E [y8] - 938.4214+[5] 22065 hepatocyte growth factor R.VQLSPDLLATLPEPA 981.038 P [y8] -810.4104+[1] 51109 activator SPGR.Q 7++

HGFA_HUMAN Q[b2]-228.1343+[2] 19063 hepatocyte growth factor R.TTDVTQTFGIEK.Y 670.340 D[b3]-318.1296+[1] 104844 activator 6++

HGFA_HUMAN T [y8] - 923.4833+[2] 93287

T [b2] - 203.1026+[3] 72498

D [ylO] - 1137.5786+[4] 53886

1 [y3] - 389.2395+[5] 53811

Q [y7] - 822.4356+[6] 42253

V[b4]-417.1980+[7] 38726

T [y6] - 694.3770+[8] 36474

F [y5] - 593.3293+[9] 26793

E [y2]-276.1554+[10] 24616

G[y4]-446.2609+[ll] 22215

V [y9] - 1022.5517+[12] 20564 hepatocyte growth factor R.EALVPLVADHK.C 596.340 P [y7] - 779.4410+[1] 57992 activator 2++

HGFA_HUMAN L[b3]-314.1710+[2] 42740 hepatocyte growth factor R.EALVPLVADHK.C 397.895 P [y7] - 390.2241++[1] 502380 activator 9+++

HGFA_HUMAN V [y5] - 569.3042+[2] 108586

V[y8]-439.7584++[3] IOOOOI

H [y2] - 284.1717+[4] 71234

L[y9]-496.3004++[5] 65572

A [y4] - 470.2358+ [6] 62284 hepatocyte growth factor R.LHKPGVYTR.V 357.541 P [y6] - 692.3726+[l] 104812 activator 7+++

HGFA_HUMAN H [y8] -479.2669++ [2] 49302

K[y7]-410.7374++[3] 30859

Y [y3] - 439.2300+[4] 23829 hepatocyte growth factor R.VANYVDWINDR.I 682.833 D[y6]-818.3791+[1] 132314 activator 3++

HGFA_HUMAN V [y7] -917.4476+[2] 81805 Protein Peptide m/z, fragment ion, m/z, charge, area charge rank

N [b3]- 285.1557+[3] 70622

W [y5] - 703.3522+[4] 53586

N [y3]-404.1888+[5] 37675

A [b2] - 171.1128+[6] 36474 alpha-l-antichymotrypsin R.GTHVDLGLASANVD 1113. L [b6] - 623.3148+[1] 244118

FAFSLYK.Q 0655++

AACT_HUMAN L [b8] - 793.4203+[2] 211429

H [b3]-296.1353+[3] 204581

D[b5]-510.2307+[4] 200032

S [y4] - 510.2922+[5] 195904

V [b4] - 395.2037+[6] 187415

A[b9]-864.4574+[7] 167905

G [b7] - 680.3362+[8] 87564

Y[y2]-310.1761+[9] 74385

F [y7] - 875.4662+[10] 50794

F [y5] - 657.3606+[ll] 44462

S [blO] -951.4894+[12] 43899

D [y8] - 990.4931+[13] 39866

A [y6] - 728.3978+[14] 33300

A [bll] - 1022.5265+[15] 32502

L [y3] - 423.2602+[16] 29829

V [y9] - 1089.5615+[17] 22043

N [bl2] - 1136.5695+[18] 17353 alpha-l-antichymotrypsin R.GTHVDLGLASANVD 742.3 D[y8]-990.4931+[1] 830612

FAFSLYK.Q 794+++

AACT HUMAN L [b8] - 793.4203+[2] 635646

G [b7] - 680.3362+[3] 582273

S [y4] - 510.2922+[4] 548645

D[b5]-510.2307+[5] 471071

F [y7] - 875.4662+[6] 420278

A[b9]-864.4574+[7] 411366

A [y6] - 728.3978+[8] 391668

Y[y2]-310.1761+[9] 390214

F [y5] - 657.3606+[10] 358134

T [b2] - 159.0764+[11] 288721

H [b3]-296.1353+[12] 251998

L [b6] - 623.3148+[13] 240742

V [y9] - 1089.5615+[14] 197218

V [b4] - 395.2037+[15] 186055

L [y3] - 423.2602+[16] 173673

S [blO] -951.4894+[17] 103651

N [bl2] - 1136.5695+[18] 97976

A [bll] - 1022.5265+[19] 76448 alpha-l-antichymotrypsin K.FNLTETSEAEIHQSFQ 800.7 A[b9]-993.4524+[l] 75792

HLLR.T 363+++

AACT_HUMAN L [b3] - 375.2027+[2] 59001

H [y9] - 1165.6225+[3] 57829

L [y2] - 288.2030+[4] 55343

T[b4]-476.2504+[5] 19323 alpha-l-antichymotrypsin K.EQLSLLDR.F 487.2 S [y5] - 603.3461+[1] 4247034

693++

AACT_HUMAN L [y3] - 403.2300+[2] 2094711

L [y6] - 716.4301+[3] 1465135

L [y4] - 516.3140+[4] 1365427 Protein Peptide m/z, fragment ion, m/z, charge, area charge rank

Q[b2]-258.1084+[5] 1222196

D [y2] - 290.1459+[6] 957403

L[b3]- 371.1925+[7] 114810 alpha-l-antichymotrypsin K.EQLSLLDR.F 325.1 L [y3] - 403.2300+[l] 57123

819+++

AACT_HUMAN D [y2] - 290.1459+[2] 52105 alpha-l-antichymotrypsin K.YTGNASALFILPDQD 876.9 L [y9] - 1088.5986+[1] 39933

K.M 438++

AACT_HUMAN A [b5] - 507.2198+[2] 20117

D [y4] - 505.2253+[3] 19937 alpha-l-antichymotrypsin R.EIGELYLPK.F 531.2 P [y2] -244.1656+[1] 8170395

975++

AACT HUMAN G [y7]-819.4611+[2] 3338199

L [y5] - 633.3970+[3] 2616703

L [y3] - 357.2496+[4] 1922561

Y [y4] - 520.3130+[5] 1527792

G [b3]-300.1554+[6] 1417240

1 [b2]-243.1339+[7] 1097654

E [y6] - 762.4396+[8] 302412

E [b4]-429.1980+[9] 81633

Y [b6] - 705.3454+[10] 36795

L[b5]-542.2821+[11] 31993 alpha-l-antichymotrypsin R.EIGELYLPK.F 354.5 P [y2] -244.1656+[1] 189758

341+++

AACT_HUMAN L [y3] - 357.2496+[2] 86952

G [b3]-300.1554+[3] 49661

Y [y4] - 520.3130+[4] 45518

E [b4] -429.1980+[5] 19576

1 [b2]- 243.1339+16] 18375

L [b5] - 542.2821+[7] 13091 alpha-l-antichymotrypsin R.DYNLNDILLQLGIEEA 1148. G [y9]-981.4888+[l] 378153

FTSK.A 5890++

AACT_HUMAN F [bl7] - 981.4964++[2] 378153

N [b3]-393.1405+[3] 338897

L [ylO] - 1094.5728+[4] 283255

E [y7] - 811.3832+[5] 180253

1 [b7] -848.3785+[6] 172510

T[y3]-335.1925+[7] 162966

D [b6] - 735.2944+[8] 135235

L [b4] - 506.2245+[9] 131573

A [y5] - 553.2980+[10] 129232

F [y4] - 482.2609+ [11] 124490

Y [b2] - 279.0975+[12] 115367

L [b9] - 1074.5466+[13] 106363

L [b8] - 961.4625+[14] 101621

E [y6] - 682.3406+[15] 98740

S[y2]-234.1448+[16] 75991

N [b5] - 620.2675+[17] 66387

1 [y8] - 924.4673+[18] 61465 alpha-l-antichymotrypsin R.DYNLNDILLQLGIEEA 766.0 G [y9]-981.4888+[l] 309485

FTSK.A 618+++

AACT_HUMAN F [bl7] - 981.4964++[2] 309485

E [y7] - 811.3832+[3] 262306

N [b3]-393.1405+[4] 212306 Protein Peptide m/z, fragment ion, m/z, charge, area charge rank

T[y3]-335.1925+[5] 199100

F [y4] - 482.2609+ [6] 164346

A [y5] - 553.2980+[7] 161405

Y [b2] - 279.0975+[8] 149220

E [y6] - 682.3406+[9] 138836

L [ylO] - 1094.5728+[10] 137336

S[y2]-234.1448+[11] 134094

1 [b7]-848.3785+[12] 80072

1 [y8] - 924.4673+[13] 77791

L [b4] - 506.2245+[14] 70889

D [b6] - 735.2944+[15] 64706

L [b8] - 961.4625+[16] 51201

N [b5] - 620.2675+[17] 42677

L [b9] - 1074.5466+[18] 21609 alpha-l-antichymotrypsin K.ADLSGITGAR.N 480.7 S [y7] - 661.3628+[1] 4360743

591++

AACT_HUMAN G [y6] - 574.3307+ [2] 3966462

T [y4] - 404.2252+[3] 1937824

D [b2] - 187.0713+[4] 799907

G [y3]-303.1775+[5] 647883

1 [y5] - 517.3093+[6] 612145

L [b3] - 300.1554+[7] 606995

S [b4] -387.1874+[8] 544408

L [y8] - 774.4468+[9] 348247

G [b5] - 444.2089+[10] 232083

1 [b6]-557.2930+[ll] 132531

A[y2]-246.1561+[12] 113896 alpha-l-antichymotrypsin K.ADLSGITGAR.N 320.8 T [y4] - 404.2252+[l] 218597

418+++

AACT_HUMAN G [y3]-303.1775+[2] 159381

G [b5]-444.2089+[3] 46527

A[y2]-246.1561+[4] 26911

D [b2] - 187.0713+[5] 22497

S [b4] -387.1874+[6] 14589 alpha-l-antichymotrypsin R.NLAVSQWHK.A 547.8 L[b2]-228.1343+[1] 1872233

195++

AACT_HUMAN A[y8]-867.5047+[2] 1133381

A[b3]-299.1714+[3] 1126331

V [y7] - 796.4676+[4] 672341

S [y6] - 697.3991+[5] 650028

H [y2]-284.1717+[6] 582720

V [y3] - 383.2401+[7] 211547

V [b4] - 398.2398+[8] 163917

Q [y5] - 610.3671+[9] 100778

V [y4] - 482.3085+[10] 88456

S [b5] -485.2718+[11] 64488

V[b7]-712.3988+[12] 36045 alpha-l-antichymotrypsin R.NLA SQVVHK.A 365.5 L [b2] - 228.1343+[1] 1175923

487+++

AACT_HUMAN V [y3] - 383.2401+[2] 593693

S [y6] - 697.3991+[3] 587502

H [y2]-284.1717+[4] 440259

V [y4] - 482.3085+[5] 375955

Q [y5] - 610.3671+[6] 349044 Protein Peptide m/z, fragment ion, m/z, charge, area charge rank

A [b3] - 299.1714+[7] 339236

V [b4] - 398.2398+[8] 172805

S [b5] - 485.2718+[9] 84594 alpha-l-antichymotrypsin K.AVLDVFEEGTEASAA 954.4 D [b4] - 399.2238+[l] 1225699

TAVK.I 835++

AACT_H UMAN G [yll] - 1005.5211+[2] 812780

V [b5] - 498.2922+[3] 741243

E [yl2] - 1134.5637+[4] 651070

V [b2] - 171.1128+[5] 634335

A [y8] - 718.4094+[6] 416106

S [y7] - 647.3723+[7] 360507

F [b6] - 645.3606+[8] 293935

T [y4] - 418.2660+[9] 281736

E [y9] - 847.4520+[10] 247592

A [y3] - 317.2183+[11] 246550

E [b7] - 774.4032+[12] 234044

T [ylO] - 948.4997+[13] 221478

A [y6] - 560.3402+[14] 212344

A [y5] - 489.3031+[15] 195364

E [b8] - 903.4458+[16] 183901

L [b3] - 284.1969+[17] 176116

V [y2] - 246.1812+[18] 157419

T [blO] - 1061.5150+[19] 52841

E [bll] - 1190.5576+[20] 34757

G [b9] - 960.4673+[21] 25807 alpha-l-antichymotrypsin K.AVLDVFEEGTEASAA 636.6 V [b2] - 171.1128+[l] 659591

TAVK.I 581+++

AACT_H UMAN S [y7] - 647.3723+[2] 630596

A [y8] - 718.4094+[3] 509467

D [b4] - 399.2238+[4] 353335

A [y6] - 560.3402+[5] 306747

A [y5] - 489.3031+[6] 280878

E [y9] - 847.4520+[7] 247347

T [y4] - 418.2660+[8] 197203

A [y3] - 317.2183+[9] 128853

V [b5] - 498.2922+[10] 120271

V [y2] - 246.1812+[11] 115428

L [b3] - 284.1969+[12] 102984

G [yll] - 1005.5211+[13] 91215

F [b6] - 645.3606+[14] 79016

E [yl2] - 1134.5637+[15] 72947

E [b7] - 774.4032+[16] 58358

T [ylO] - 948.4997+[17] 41071

E [b8] - 903.4458+[18] 32918

G [b9] - 960.4673+[19] 24275 alpha-l-antichymotrypsin K.ITLLSALVETR.T 608.3 S [y7] - 775.4308+[l] 7387615

690++

AACT_H UMAN T [b2] - 215.1390+[2] 3498457

L [y8] - 888.5149+[3] 2684639

L [b3] - 328.2231+[4] 2164246

A [y6] - 688.3988+[5] 2045853

L [y5] - 617.3617+[6] 2027311

L [y9] - 1001.5990+[7] 1949318

V [y4] - 504.2776+[8] 1598519 Protein Peptide m/z, fragment ion, m/z, charge, area charge rank

T[y2]-276.1666+[9] 1416847

E [y3] - 405.2092+[10] 967259

A [b6] - 599.3763+[ll] 579420

L [b4] - 441.3071+[12] 431556

S [b5] - 528.3392+[13] 107634

L [b7] - 712.4604+[14] 71104

V[b8]-811.5288+[15] 24197 alpha-l-antichymotrypsin K.ITLLSALVETR.T 405.9 E [y3] - 405.2092+[l] 738128

151+++

AACT HUMAN T[y2]-276.1666+[2] 368830

V [y4] - 504.2776+[3] 328133

A [b6] - 599.3763+[4] 132469

T[b2]-215.1390+[5] 126898

L [v5] - 617.3617+[6] 124559

S [y7] - 775.4308+[7] 54263

L [b3] - 328.2231+[8] 37891

A [y6] - 688.3988+[9] 29853

L [b4] - 441.3071+[10] 25558

L [b7] - 712.4604+[11] 13353

S [b5] -528.3392+[12] 12290

Pigment epithelium- K. LAAAVS N F GYD LYR . 780.3 D[bll]- 1109.5262+[l] 136227 derived factor V 963++

PEDF_HUMAN* F [b8] - 774.4145+[2] 61248

N [b7]-314.1767++[3] 55532

A [yl2] - 1375.6641+[4] 53268

V[b5]-213.6392++[5] 35818

L [bl2] - 1222.6103+[6] 34918

G [b9]-831.4359+[7] 33934

Y[bl0]-994.4993+[8] 32923

G [b9]-416.2216++[9] 32650

V[b5]-426.2711+[10] 15646

A[b2]- 185.1285+[ll] 14964

D [bll] - 555.2667++[12] 13922

L [y3] - 226.1368++[13] 13027

A [b4] - 327.2027+[14] 12782

A [yl2] - 688.3357++[15] 12446

V [ylO] - 1233.5899+[16] 12400

A [yll] - 652.8171++[17] 10793

Pigment epithelium- K. LAAAVS NF GYD LYR. 520.5 G [y6] - 786.3781+ [1] 42885 derived factor V 999+++

PEDF_HUMAN* D [y4] - 566.2933+[2] 32080

Y [y5] - 729.3566+[3] 17494

L [y3] - 451.2663+[5] 12304

Y[y2]-338.1823+[6] 7780

Pigment epithelium- R.ALYYDLISSPDIHGTY 652.6 Y [yl5] - 886.4305++[l] 12278 derived factor K.E 632+++

PEDF_HUMAN* L[b2]- 185.1285+[2] 7601

S [ylO] - 1104.5320+[3] 7345

Y [yl4] - 804.8988++[4] 5976

Pigment epithelium- K.ELLDTVTAPQK.N 607.8 T [y5] - 272.6581++[1] 59670 derived factor 350++

PEDF_HUMAN* Q[y2]-275.1714+[2] 11954

Pigment epithelium- K.ELLDTVTAPQK.N 405.5 L [b2] - 243.1339+[1] 16428 derived factor 591+++ Protein Peptide m/z, fragment ion, m/z, charge, area charge rank

PEDF_H UMAN* T [b7] - 386.7080++[2] 7918

Q [y2] - 275.1714+[3] 7043

T [v5] - 272.6581++[4] 5237

Pigment epithelium- K.SSFVAPLEK.S 489.2 A [v5] - 557.3293+[l] 20068 derived factor 687++

PEDF_H UMAN* A [y5] - 279.1683++[2] 5059

S [b2] - 175.0713+[3] 4883

Pigment epithelium- K.SSFVAPLEK.S 326.5 A [y5] - 279.1683++[1] 70240 derived factor 149+++

PEDF_H UMAN* A [v5] - 557.3293+[2] 63329

S [b2] - 175.0713+[3] 39662

L [b7] - 351.6947++[4] 5393

Pigment epithelium- K.EIPDEISILLLGVAHFK 632.0 P [yl5] - 826.4745++[l] 37871 derived factor .G 277+++

PEDF_H UMAN* G [y6] - 658.3671+[2] 20077

L [y7] - 771.4512+[3] 8952

Pigment epithelium- K.TSLEDFYLDEER.T 758.8 R [yl] - 175.1190+[1] 8206 derived factor 437++

PEDF_H UMAN* D [b9] - 1084.4833+[2] 4591

F [b6] - 693.3090+[3] 4498

Pigment epithelium- K.TSLEDFYLDEER.T 506.231 F [b6] - 693.3090+[l] 3526 derived factor 6+++

PEDF_H UMAN* D [y4] - 548.2311+[2] 3208

Pigment epithelium- K.VTQNLTLIEESLTSEFI 858.4 T [bl3] - 721.8905++[1] 11072 derived factor HDIDR.E 413+++

PEDF_H UMAN* T [yl7] - 1009.5075++[2] 8442

D [y4] - 518.2569+[3] 6522

Pigment epithelium- K.TVQAVLTVPK.L 528.3 Q [y8] - 855.5298+[l] 83536 derived factor 266++

PEDF_H UMAN* V [b2] - 201.1234+[2] 64729

A [b4] - 200.6132++[3] 58198

P [y2] - 244.1656+[4] 43347

Q [y8] - 428.2686++[5] 38398

A [y7] - 727.4713+[6] 33770

Q [b3] - 329.1819+[7] 17809

L [y5] - 557.3657+[8] 17518

V [y6] - 656.4341+[9] 17029

V [y6] - 328.7207++[10] 15839

T [y4] - 444.2817+[11] 13859

V [y3] - 343.2340+[12] 10717

A [b4] - 400.2191+[13] 9695

Pigment epithelium- K.TVQAVLTVPK.L 352.5 P [y2] - 244.1656+[1] 8295 derived factor 535+++

PEDF_H UMAN* T [y4] - 444.2817+[2] 2986

A [b4] - 400.2191+[3] 2848

Pigment epithelium- K.LSYEGEVTK.S 513.2 V [b7] - 389.6845++[l] 60831 derived factor 611++

PEDF_H UMAN* E [b6] - 679.2933+[2] 34857

Y [y7] - 413.2031++[3] 10075

V [b7] - 778.3618+[4] 8920

Y [b3] - 364.1867+[5] 8008

Pigment epithelium- K.LQSLFDSPDFSK.I 692.3 S [y2] - 234.1448+ [1] 49594 derived factor 432++

PEDF_H UMAN* L [y9] - 1055.5044+[2] 48160 Protein Peptide m/z. fragment ion, m/z, charge, area charge rank

P [b8] - 888.4462+[3] 23566

S [b7] - 791.3934+[4] 13766

P [v5] - 297.1501++[5] 12305

P [y5] - 593.2930+[6] 10702

F [b5] - 589.3344+[7] 8929

D [b9] - 1003.4731+[8] 8742

Pigment epithelium- K.LQSLFDSPDFSK.I 461.8 P [y5] - 593.2930+[l] 9154 derived factor 979+++

PEDF_H UMAN* P [y5] - 297.1501++[2] 5479

Pigment epithelium- R.DTDTGALLFIGK.I 625.8 G [v2] - 204.1343+ [1] 32092 derived factor 350++

PEDF_H UMAN* G [y8] - 818.5135+[2] 29707

T [b2] - 217.0819+[4] 28172

T [b4] - 217.0819++[3] 28172

F [y4] - 464.2867+ [5] 22160

D [ylO] - 1034.5881+[6] 20267

T [y9] - 919.5611+[7] 17083

L [y6] - 690.4549+[8] 14854

L [y5] - 577.3708+[9] 12349

T [b4] - 433.1565+[10] 11773

1 [y3] - 317.2183+[ll] 11575

D [b3] - 332.1088+[12] 8968

A [y7] - 761.4920+[13] 8598

* Transition scan on Agilent 6490

Example 4. Study III to Identify and Confirm Preeclampsia Biomarkers

[00167] A further hypothesis-dependent study was performed using essentially the same methods described in the preceding Examples unless noted below. The scheduled MRM assay used in Examples 1 and 2 but now augmented with newly discovered analytes from the Example 3 and related studies was used. Less robust transitions (from the original 1708 described in Example 1) were removed to improve analytical performance and make room for the newly discovered analytes.

[00168] Thirty subjects with preeclampsia who delivered preterm (<37 weeks 0 days) were selected for analyses. Twenty-three subjects were available with isolated preeclampsia; thus, eight subjects were selected with additional findings as follows: 5 subjects with gestational diabetes, one subject with pre-existing type 2 diabetes, and one subject with chronic hypertension. Subjects were classified as having severe preeclampsia if it was indicated in the Case Report Form as severe or if the pregnancy was complicated by HELLP syndrome. All other cases were classified as mild preeclampsia. Cases were matched to term controls (>/= 37 weeks 0 days) without preeclampsia at a 2:1 control-to-case ratio. [00169] The samples were processed in 4 batches with each containing 3 HGS controls. All serum samples were depleted of the 14 most abundant serum proteins using MARS 14 (Agilent), digested with trypsin, desalted, and resolubilized with reconstitution solution containing 5 internal standard peptides as described in previous examples.

[00170] The LC-MS/MS analysis was performed with an Agilent Poroshell 120 EC-C 18 column (2.1x50mm, 2.7 μιη) at a flow rate of 400 μΐ/min and eluted with an acetonitrile gradient into an AB Sciex QTRAP5500 mass spectrometer. The sMRM assay measured 750 transitions that correspond to 349 peptides and 164 proteins. Chromatographic peaks were integrated using MultiQuantTM software (AB Sciex).

[00171] Transitions were excluded from analysis if they were missing in more than 20% of the samples. Log transformed peak areas for each transition were corrected for run order and batch effects by regression. The ability of each analyte to separate cases and controls was determined by calculating univariate AUC values from ROC curves. Ranked univariate AUC values (0.6 or greater) are reported for individual gestational age window sample sets or various combinations (Tables 12-15). Multivariate classifiers were built by Lasso and Random Forest methods. 1000 rounds of bootstrap resampling were performed and the nonzero Lasso coefficients or Random Forest Gini importance values were summed for each analyte amongst panels with AUCs of 0.85 or greater. For summed Random Forest Gini Importance values an Empirical Cumulative Distribution Function was fitted and

probabilities (P) were calculated. The nonzero Lasso summed coefficients calculated from the different window combinations are shown in Tables 16-19. Summed Random Forest Gini values, with P >0.9 are found in Tables 20-22.

[00172] Table 12. Univariate AUC values all windows

Transition Protein AUC

VNHVTLSQPK 374.9 244.2 B2MG HUMAN 0.733

VEHSDLSFSK 383.5 468.2 B2MG HUMAN 0.732

ALALPPLGLAPLLNLWAKPQGR 770.5 256.2 SHBG HUMAN 0.728

HHGPTITAK 321.2 432.3 AMBP HUMAN 0.728

FLYHK 354.2 447.2 AMBP HUMAN 0.722

FLYHK 354.2 284.2 AMBP HUMAN 0.721

IALGGLLFPASNLR 481.3 657.4 SHBG HUMAN 0.719

GDTYPAELYITGSILR 885.0 274.1 F13B HUMAN 0.716

VEHSDLSFSK 383.5 234.1 B2MG HUMAN 0.714

GPGEDFR 389.2 623.3 PTGDS HUMAN 0.714

IALGGLLFPASNLR 481.3 412.3 SHBG HUMAN 0.712

EVFSKPISWEELLQ 852.9 260.2 FA40A HUMAN 0.708

FICPLTGLWPINTLK 887.0 685.4 APOH HUMAN 0.707

GFQALGDAADIR 617.3 717.4 TIMP1 HUMAN 0.707

DVLLLVHNLPQNLTGHIWYK 791.8 310.2 PSG7 HUMAN 0.704

VVLSSGSGPGLDLPLVLGLPLQLK 791.5 598.4 SHBG HUMAN 0.704

ATVVYQGER 511.8 652.3 APOH HUMAN 0.702

ALALPPLGLAPLLNLWAKPQGR 770.5 457.3 SHBG HUMAN 0.702

VVLSSGSGPGLDLPLVLGLPLQLK 791.5 768.5 SHBG HUMAN 0.702

DVLLLVHNLPQNLTGHIWYK 791.8 883.0 PSG7 HUMAN 0.702

AHYDLR 387.7 566.3 FETUA HUMAN 0.701

GPGEDFR 389.2 322.2 PTGDS HUMAN 0.701

FSVVYAK 407.2 579.4 FETUA HUMAN 0.701

TLAFVR 353.7 274.2 FA7 HUMAN 0.699

IAPQLSTEELVSLGEK 857.5 533.3 AFAM HUMAN 0.698

HFQNLGK 422.2 527.2 AFAM HUMAN 0.696

GDTYPAELYITGSILR 885.0 922.5 F13B HUMAN 0.694

FICPLTGLWPINTLK 887.0 756.9 APOH HUMAN 0.694

EVFSKPISWEELLQ 852.9 376.2 FA40A HUMAN 0.692

ATVVYQGER 511.8 7 1.4 APOH HUMAN 0.690

ELIEELVNITQNQK 557.6 618.3 IL13 HUMAN 0.690

VNHVTLSQPK 374.9 459.3 B2MG HUMAN 0.687

IAQYYYTFK 598.8 395.2 F13B HUMAN 0.685

IAPQLSTEELVSLGEK 857.5 333.2 AFAM HUMAN 0.685

LIENGYFHPVK 439.6 627.4 F13B HUMAN 0.684

FSVVYAK 407.2 381.2 FETUA HUMAN 0.684

HFQNLGK 422.2 285.1 AFAM HUMAN 0.684

AHYDLR 387.7 288.2 FETUA HUMAN 0.684

ELPQSIVYK 538.8 417.7 FBLN3 HUMAN 0.683

DADPDTFFAK 563.8 825.4 AFAM HUMAN 0.679

DADPDTFFAK 563.8 302.1 AFAM HUMAN 0.676

IAQYYYTFK 598.8 884.4 F13B HUMAN 0.673

VVESLAK 373.2 646.4 IBP1 HUMAN 0.673

YGIEEHGK 311.5 599.3 CXA1 HUMAN 0.673

GFQALGDAADIR 617.3 288.2 TIMP1 HUMAN 0.673

YTTEIIK 434.2 704.4 C1R HUMAN 0.671

LPDTPQGLLGEAR 683.87 427.2 EGLN HUMAN 0.666 Transition Protein AUC

TLAFVR 353.7 492.3 FA7 HUMAN 0.666

LIENGYFHPVK 439.6 343.2 F13B HUMAN 0.665

ELIEELVNITQNQK 557.6 517.3 IL13 HUMAN 0.665

DPNGLPPEAQK 583.3 669.4 RET4 HUMAN 0.664

TNTNEFLIDVDK 704.85 849.5 TF HUMAN 0.663

NTVISVNPSTK 580.3 845.5 VCAM1 HUMAN 0.662

YEFLNGR 449.7 293.1 PLMN HUMAN 0.662

AIGLPEELIQK 605.86 856.5 FABPL HUMAN 0.662

YTTEIIK 434.2 603.4 C1R HUMAN 0.661

AEHPTWGDEQLFQTTR 639.3 765.4 PGH1 HUMAN 0.658

HTLNQIDEVK 598.8 951.5 FETUA HUMAN 0.658

HTLNQIDEVK 598.8 958.5 FETUA HUMAN 0.656

LPNNVLQEK 527.8 730.4 AFAM HUMAN 0.655

DPNGLPPEAQK 583.3 497.2 RET4 HUMAN 0.655

TFLTVYWTPER 706.9 401.2 ICAM1 HUMAN 0.653

TFLTVYWTPER 706.9 502.3 ICAM1 HUMAN 0.653

SEPRPGVLLR 375.2 454.3 FA7 HUMAN 0.652

FTFTLHLETPKPSISSSNLNPR 829.4 787.4 PSG1 HUMAN 0.652

DAQYAPGYDK 564.3 813.4 CFAB HUMAN 0.651

ALDLSLK 380.2 185.1 ITIH3 HUMAN 0.651

NCSFSIIYPVVIK 770.4 555.4 CRHBP HUMAN 0.650

NTVISVNPSTK 580.3 732.4 VCAM1 HUMAN 0.649

IPSNPSHR 303.2 610.3 FBLN3 HUMAN 0.649

DAQYAPGYDK 564.3 315.1 CFAB HUMAN 0.647

TLPFSR 360.7 506.3 LYAM1 HUMAN 0.647

LPNNVLQEK 527.8 844.5 AFAM HUMAN 0.644

AALAAFNAQNNGSNFQLEEISR 789.1 746.4 FETUA HUMAN 0.644

AEHPTWGDEQLFQTTR 639.3 569.3 PGH1 HUMAN 0.644

NNQLVAGYLQGPNVNLEEK 700.7 999.5 IL1RA HUMAN 0.642

EHSSLAFWK 552.8 267.1 APOH HUMAN 0.642

ALNHLPLEYNSALYSR 621.0 696.4 C06 HUMAN 0.641

VSEADSSNADWVTK 754.9 347.2 CFAB HUMAN 0.641

NFPSPVDAAFR 610.8 959.5 HEMO HUMAN 0.641

WNFAYWAAHQPWSR 607.3 545.3 PRG2 HUMAN 0.638

WNFAYWAAHQPWSR 607.3 673.3 PRG2 HUMAN 0.638

TAVTANLDIR 537.3 802.4 CHL1 HUMAN 0.638

IPSNPSHR 303.2 496.3 FBLN3 HUMAN 0.637

YWGVASFLQK 599.8 849.5 RET4 HUMAN 0.637

ALDLSLK 380.2 575.3 ITIH3 HUMAN 0.636

YNSQLLSFVR 613.8 508.3 TFR1 HUMAN 0.636

EHSSLAFWK 552.8 838.4 APOH HUMAN 0.635

YWGVASFLQK 599.8 350.2 RET4 HUMAN 0.635

ALNHLPLEYNSALYSR 621.0 538.3 C06 HUMAN 0.633

DLYHYITSYVVDGEIIIYGPAYSGR 955.5 707.3 PSG1 HUMAN 0.633

FTFTLHLETPKPSISSSNLNPR 829.4 874.4 PSG1 HUMAN 0.633

YQISVNK 426.2 560.3 FIBB HUMAN 0.632

YEFLNGR 449.7 606.3 PLMN HUMAN 0.632 Transition Protein AUC

LNIGYIEDLK 589.3 950.5 PAI2 HUMAN 0.631

LLEVPEGR 456.8 356.2 CIS HUMAN 0.630

ENPAVIDFELAPIVDLVR 670.7 811.5 C06 HUMAN 0.630

YYLQGAK 421.7 516.3 ITIH4 HUMAN 0.630

ITGFLKPGK 320.9 301.2 LBP HUMAN 0.629

DLHLSDVFLK 396.2 260.2 C06 HUMAN 0.629

HELTDEELQSLFTNFANWDK 817.1 854.4 AFAM HUMAN 0.629

YYLQGAK 421.7 327.1 ITIH4 HUMAN 0.628

NCSFSIIYPVVIK 770.4 831.5 CRHBP HUMAN 0.627

FLNWIK 410.7 560.3 HABP2 HUMAN 0.627

ITGFLKPGK 320.9 429.3 LBP HUMAN 0.627

VVESLAK 373.2 547.3 IBP1 HUMAN 0.627

NFPSPVDAAFR 610.8 775.4 HEMO HUMAN 0.627

AEIEYLEK 497.8 552.3 LYAM1 HUMAN 0.627

ENPAVIDFELAPIVDLVR 670.7 601.4 C06 HUMAN 0.627

VQEVLLK 414.8 373.3 HYOU1 HUMAN 0.626

TQIDSPLSGK 523.3 703.4 VCAM1 HUMAN 0.626

VSEADSSNADWVTK 754.9 533.3 CFAB HUMAN 0.625

DFNQFSSGEK 386.8 189.1 FETA HUMAN 0.624

LPDTPQGLLGEAR 683.87 940.5 EGLN HUMAN 0.623

DLYHYITSYVVDGEIIIYGPAYSGR 955.5 650.3 PSG1 HUMAN 0.623

FAFNLYR 465.8 712.4 HEP2 HUMAN 0.623

LLELTGPK 435.8 644.4 A1BG HUMAN 0.623

NEIVFPAGILQAPFYTR 968.5 357.2 ECE1 HUMAN 0.623

EFDDDTYDNDIALLQLK 1014.48 501.3 TP A HUMAN 0.621

FSLVSGWGQLLDR 493.3 403.2 FA7 HUMAN 0.621

LLELTGPK 435.8 227.2 A1BG HUMAN 0.621

LIQDAVTGLTVNGQITGDK 972.0 640.4 ITIH3 HUMAN 0.621

QGHNSVFLIK 381.6 520.4 HEMO HUMAN 0.620

ILPSVPK 377.2 244.2 PGH1 HUMAN 0.620

STLFVPR 410.2 272.2 PEPD HUMAN 0.620

TLEAQLTPR 514.8 685.4 HEP2 HUMAN 0.619

QGHNSVFLIK 381.6 260.2 HEMO HUMAN 0.619

LSSPAVITDK 515.8 743.4 PLMN HUMAN 0.618

LLEVPEGR 456.8 686.4 CIS HUMAN 0.617

GVTGYFTFNLYLK 508.3 260.2 PSG5 HUMAN 0.617

EALVPLVADHK 397.9 390.2 HGFA HUMAN 0.616

SFRPFVPR 335.9 272.2 LBP HUMAN 0.616

DFNQFSSGEK 386.8 333.2 FETA HUMAN 0.616

GSLVQASEANLQAAQDFVR 668.7 735.4 ITIH1 HUMAN 0.616

ITLPDFTGDLR 624.3 920.5 LBP HUMAN 0.615

LIQDAVTGLTVNGQITGDK 972.0 798.4 ITIH3 HUMAN 0.615

ILPSVPK 377.2 227.2 PGH1 HUMAN 0.614

DIIKPDPPK 11.8 342.2 IL12B HUMAN 0.613

QGFGNVATNTDGK 654.81 319.2 FIBB HUMAN 0.613

AVLHIGEK 289.5 348.7 THBG HUMAN 0.613

YENYTSSFFIR 713.8 756.4 IL12B HUMAN 0.613 Transition Protein AUC

LSSPAVITDK 515.8 830.5 PLMN HUMAN 0.613

SFRPFVPR 335.9 635.3 LBP HUMAN 0.613

GLQYAAQEGLLALQSELLR 1037.1 858.5 LBP HUMAN 0.612

VELAPLPSWQPVGK 760.9 400.3 ICAM1 HUMAN 0.612

CRPINATLAVEK 457.9 559.3 CGB1 HUMAN 0.610

GIVEECCFR 585.3 771.3 IGF2 HUMAN 0.610

AVLHIGEK 289.5 292.2 THBG HUMAN 0.610

TLEAQLTPR 514.8 814.4 HEP2 HUMAN 0.610

SILFLGK 389.2 577.4 THBG HUMAN 0.609

HVVQLR 376.2 614.4 IL6RA HUMAN 0.609

TQILEWAAER 608.8 761.4 EGLN HUMAN 0.609

NSDQEIDFK 548.3 409.2 S 10A5 HUMAN 0.609

S GAQ AT WTELP WPHEK 613.3 510.3 HEMO HUMAN 0.607

EDTPNSVWEPAK 686.8 630.3 CIS HUMAN 0.607

ITLPDFTGDLR 624.3 288.2 LBP HUMAN 0.607

TLPFSR 360.7 409.2 LYAM1 HUMAN 0.607

GIVEECCFR 585.3 900.3 IGF2 HUMAN 0.606

S GAQ AT WTELP WPHEK 613.3 793.4 HEMO HUMAN 0.606

VRPQQLVK 484.3 609.4 ITIH4 HUMAN 0.605

SEYGAALAWEK 612.8 788.4 C06 HUMAN 0.605

LEEHYELR 363.5 288.2 PAI2 HUMAN 0.605

FQLPGQK 409.2 275.1 PSG1 HUMAN 0.605

IHWESASLLR 606.3 437.2 C03 HUMAN 0.604

NAVVQGLEQPHGLWHPLR 688.4 890.6 LRP1 HUMAN 0.604

VTGLDFIPGLHPILTLSK 641.04 771.5 LEP HUMAN 0.603

YNSQLLSFVR 613.8 734.5 TFR1 HUMAN 0.603

ALVLELAK 428.8 672.4 INHBE HUMAN 0.603

FAFNLYR 465.8 565.3 HEP2 HUMAN 0.603

VRPQQLVK 484.3 722.4 ITIH4 HUMAN 0.602

SLQAFVAVAAR 566.8 487.3 IL23A HUMAN 0.602

AGFAGDDAPR 488.7 701.3 ACTB HUMAN 0.601

EDTPNSVWEPAK 686.8 315.2 CIS HUMAN 0.601

VQEVLLK 414.8 601.4 HYOU1 HUMAN 0.601

SEYGAALAWEK 612.8 845.5 C06 HUMAN 0.601

TLFIFGVTK 513.3 215.1 PSG4 HUMAN 0.601

YNQLLR 403.7 288.2 ENOA HUMAN 0.600

TQIDSPLSGK 523.3 816.5 VCAM1 HUMAN 0.600

[00173] Table 13. Univariate AUC values early window

Transition Protein AUC

VEHSDLSFSK 383.5 234.1 B2MG HUMAN 0.778

TVQAVLTVPK 528.3 428.3 PEDF HUMAN 0.775

TVQAVLTVPK 528.3 855.5 PEDF HUMAN 0.775

DTDTGALLFIGK 625.8 217.1 PEDF HUMAN 0.772

ETLLQDFPv 511.3 565.3 AMBP HUMAN 0.772

DTDTGALLFIGK 625.8 818.5 PEDF HUMAN 0.769

VVESLAK 373.2 646.4 IBP1 HUMAN 0.766

FSVVYAK 407.2 381.2 FETUA HUMAN 0.764

HHGPTITAK 321.2 275.1 AMBP HUMAN 0.764

ETLLQDFPv 511.3 322.2 AMBP HUMAN 0.761

FLYHK 354.2 447.2 AMBP HUMAN 0.758

GPGEDFPv 389.2 623.3 PTGDS HUMAN 0.755

HHGPTITAK 321.2 432.3 AMBP HUMAN 0.755

VEHSDLSFSK 383.5 468.2 B2MG HUMAN 0.752

FLYHK 354.2 284.2 AMBP HUMAN 0.749

FSVVYAK 407.2 579.4 FETUA HUMAN 0.749

VNHVTLSQPK 374.9 459.3 B2MG HUMAN 0.749

IPSNPSHPv 303.2 610.3 FBLN3 HUMAN 0.746

VVESLAK 373.2 547.3 IBP1 HUMAN 0.746

IPSNPSHPv 303.2 496.3 FBLN3 HUMAN 0.746

NCSFSIIYPVVIK 770.4 555.4 CRHBP HUMAN 0.746

GFQALGDAADIPv 617.3 288.2 TIMP1 HUMAN 0.744

IQTHSTTYPv 369.5 627.3 F13B HUMAN 0.744

AALAAFNAQNNGSNFQLEEISR 789.1 746.4 FETUA HUMAN 0.738

AHYDLR 387.7 566.3 FETUA HUMAN 0.738

IQTHSTTYR 369.5 540.3 F13B HUMAN 0.738

AIGLPEELIQK 605.86 856.5 FABPL HUMAN 0.735

ATVVYQGER 511.8 751.4 APOH HUMAN 0.735

FICPLTGLWPINTLK 887.0 685.4 APOH HUMAN 0.735

FICPLTGLWPINTLK 887.0 756.9 APOH HUMAN 0.735

HTLNQIDEVK 598.8 958.5 FETUA HUMAN 0.735

AQETSGEEISK 589.8 979.5 IBP1 HUMAN 0.732

DSPSVWAAVPGK 607.31 301.2 PROF1 HUMAN 0.732

GPGEDFR 389.2 322.2 PTGDS HUMAN 0.732

ATVVYQGER 511.8 652.3 APOH HUMAN 0.729

NFPSPVDAAFR 610.8 959.5 HEMO HUMAN 0.729

LIENGYFHPVK 439.6 627.4 F13B HUMAN 0.726

AHYDLR 387.7 288.2 FETUA HUMAN 0.726

ELIEELVNITQNQK 557.6 618.3 IL13 HUMAN 0.724

ETPEGAEABCPWYEPIYLGGVFQLEK 951.14 877.5 TNFA HUMAN 0.724

ALDLSLK 380.2 185.1 ITIH3 HUMAN 0.721

IHWESASLLR 606.3 437.2 C03 HUMAN 0.721

DAQYAPGYDK 564.3 813.4 CFAB HUMAN 0.718

NFPSPVDAAFR 610.8 775.4 HEMO HUMAN 0.718

AVGYLITGYQR 620.8 523.3 PZP HUMAN 0.715

AVGYLITGYQR 620.8 737.4 PZP HUMAN 0.712

DIPHWLNPTR 416.9 600.3 PAPP1 HUMAN 0.712 Transition Protein AUC

ALDLSLK 380.2 575.3 ITIH3 HUMAN 0.709

IEGNLIFDPN YLPK 874.0 845.5 APOB HUMAN 0.709

LIENGYFHPVK 439.6 343.2 F13B HUMAN 0.709

QTLSWTVTP 580.8 818.4 PZP HUMAN 0.709

DAQYAPGYDK 564.3 315.1 CFAB HUMAN 0.707

GLQYAAQEGLLALQSELLR 1037.1 858.5 LBP HUMAN 0.707

IEGNLIFDPNNYLPK 874.0 414.2 APOB HUMAN 0.707

IQHPFTVEEFVLPK 562.0 861.5 PZP HUMAN 0.707

QTLSWTVTPK 580.8 545.3 PZP HUMAN 0.707

VSEADSSNADWVTK 754.9 347.2 CFAB HUMAN 0.707

ILPSVPK 377.2 244.2 PGH1 HUMAN 0.704

IQHPFTVEEFVLPK 562.0 603.4 PZP HUMAN 0.704

NCSFSIIYPVVIK 770.4 831.5 CRHBP HUMAN 0.704

YNSQLLSFVR 613.8 508.3 TFR1 HUMAN 0.704

HTLNQIDEVK 598.8 951.5 FETUA HUMAN 0.701

NEIWYR 440.7 637.4 FA 12 HUMAN 0.701

QGHNSVFLIK 381.6 260.2 HEMO HUMAN 0.701

YTTEIIK 434.2 603.4 C1R HUMAN 0.701

STLFVPR 410.2 272.2 PEPD HUMAN 0.699

EVFSKPISWEELLQ 852.9 260.2 FA40A HUMAN 0.698

TGISPLALIK 506.8 741.5 APOB HUMAN 0.698

TSESGELHGLTTEEEFVEGIYK 819.06 310.2 TTHY HUMAN 0.698

AEHPTWGDEQLFQTTR 639.3 569.3 PGH1 HUMAN 0.695

AEHPTWGDEQLFQTTR 639.3 765.4 PGH1 HUMAN 0.695

HFQNLGK 422.2 527.2 AFAM HUMAN 0.695

SVSLPSLDPASAK 636.4 473.3 APOB HUMAN 0.695

ILPSVPK 377.2 227.2 PGH1 HUMAN 0.692

LIQDAVTGLTVNGQITGDK 972.0 640.4 ITIH3 HUMAN 0.692

QGHNSVFLIK 381.6 520.4 HEMO HUMAN 0.692

TGISPLALIK 506.8 654.5 APOB HUMAN 0.692

YGIEEHGK 311.5 599.3 CXA1 HUMAN 0.692

ELIEELVNITQNQK 557.6 517.3 IL13 HUMAN 0.689

IHWESASLLR 606.3 251.2 C03 HUMAN 0.689

LIQDAVTGLTVNGQITGDK 972.0 798.4 ITIH3 HUMAN 0.689

ALALPPLGLAPLLNLWAKPQGR 770.5 256.2 SHBG HUMAN 0.687

ALNFGGIGVVVGHELTHAFDDQGR 837.1 299.2 ECE1 HUMAN 0.687

AQETSGEEISK 589.8 850.4 IBP1 HUMAN 0.687

GVTGYFTFNLYLK 508.3 683.9 PSG5 HUMAN 0.687

ITLPDFTGDLR 624.3 288.2 LBP HUMAN 0.687

LPDTPQGLLGEAR 683.87 427.2 EGLN HUMAN 0.687

SVSLPSLDPASAK 636.4 885.5 APOB HUMAN 0.687

TLAFVR 353.7 274.2 FA7 HUMAN 0.687

YTTEIIK 434.2 704.4 C1R HUMAN 0.687

EFDDDTYDNDIALLQLK 1014.48 388.3 TPA HUMAN 0.684

IALGGLLFPASNLR 481.3 657.4 SHBG HUMAN 0.684

DFNQFSSGEK 386.8 189.1 FETA HUMAN 0.681

EHSSLAFWK 552.8 838.4 APOH HUMAN 0.681 Transition Protein AUC

ELPQSIVYK 538.8 409.2 FBLN3 HUMAN 0.681

ITGFLKPGK 320.9 301.2 LBP HUMAN 0.681

ITGFLKPGK 320.9 429.3 LBP HUMAN 0.681

AFQVWSDVTPLR 709.88 385.3 MMP2 HUMAN 0.678

GLQYAAQEGLLALQSELLR 1037.1 929.5 LBP HUMAN 0.678

HYINLITR 515.3 301.1 NPY HUMAN 0.678

NAVVQGLEQPHGLWHPLR 688.4 890.6 LRP1 HUMAN 0.675

WWGGQPLWITATK 772.4 929.5 ENPP2 HUMAN 0.675

YNQLLR 403.7 288.2 ENOA HUMAN 0.675

LDGSTHLNIFFAK 488.3 852.5 PAPP1 HUMAN 0.672

VVGGLVALR 442.3 784.5 FA 12 HUMAN 0.672

WNFAYWAAHQPWSR 607.3 673.3 PRG2 HUMAN 0.672

NHYTESISVAK 624.8 252.1 NEUR1 HUMAN 0.670

NSDQEIDFK 548.3 409.2 S10A5 HUMAN 0.670

SGAQATWTELPWPHEK 613.3 510.3 HEMO HUMAN 0.670

WNFAYWAAHQPWSR 607.3 545.3 PRG2 HUMAN 0.670

SFRPFVPR 335.9 272.2 LBP HUMAN 0.670

AFQVWSDVTPLR 709.88 347.2 MMP2 HUMAN 0.667

DADPDTFFAK 563.8 825.4 AFAM HUMAN 0.667

EHSSLAFWK 552.8 267.1 APOH HUMAN 0.667

ITENDIQIALDDAK 779.9 632.3 APOB HUMAN 0.667

ITLPDFTGDLR 624.3 920.5 LBP HUMAN 0.667

VQEVLLK 414.8 373.3 HYOU1 HUMAN 0.667

VSFSSPLVAISGVALR 802.0 715.4 PAPP1 HUMAN 0.667

HFQNLGK 422.2 285.1 AFAM HUMAN 0.664

ITENDIQIALDDAK 779.9 873.5 APOB HUMAN 0.664

ALQDQLVLVAAK 634.9 289.2 ANGT HUMAN 0.661

DLHLSDVFLK 396.2 260.2 C06 HUMAN 0.661

DLHLSDVFLK 396.2 366.2 C06 HUMAN 0.661

TAVTANLDIR 537.3 802.4 CHL1 HUMAN 0.661

DADPDTFFAK 563.8 302.1 AFAM HUMAN 0.658

DPTFIPAPIQAK 433.2 461.2 ANGT HUMAN 0.658

FAFNLYR 465.8 712.4 HEP2 HUMAN 0.658

IALGGLLFPASNLR 481.3 412.3 SHBG HUMAN 0.658

IAQYYYTFK 598.8 395.2 F13B HUMAN 0.658

LPNNVLQEK 527.8 730.4 AFAM HUMAN 0.658

SLDFTELDVAAEK 719.4 874.5 ANGT HUMAN 0.658

VELAPLPSWQPVGK 760.9 400.3 ICAM1 HUMAN 0.658

DIIKPDPPK 511.8 342.2 IL12B HUMAN 0.655

EVFSKPISWEELLQ 852.9 376.2 FA40A HUMAN 0.655

LSETNR 360.2 330.2 PSG1 HUMAN 0.655

NEIWYR 440.7 357.2 FA 12 HUMAN 0.655

SFRPFVPR 335.9 635.3 LBP HUMAN 0.655

SGAQATWTELPWPHEK 613.3 793.4 HEMO HUMAN 0.655

TGAQELLR 444.3 530.3 GELS HUMAN 0.655

VSEADSSNADWVTK 754.9 533.3 CFAB HUMAN 0.655

VVGGLVALR 442.3 685.4 FA 12 HUMAN 0.655 Transition Protein AUC

DISEVVTPR 508.3 787.4 CFAB HUMAN 0.652

IHPSYTNYR 575.8 598.3 PSG2 HUMAN 0.652

VSFSSPLVAISGVALR 802.0 602.4 PAPP1 HUMAN 0.652

YNQLLR 403.7 529.3 ENOA HUMAN 0.652

ALQDQLVLVAAK 634.9 956.6 ANGT HUMAN 0.650

IHPSYTNYR 575.8 813.4 PSG2 HUMAN 0.650

TFLTVYWTPER 706.9 401.2 ICAM1 HUMAN 0.650

VQEVLLK 414.8 601.4 HYOU1 HUMAN 0.650

GDTYPAELYITGSILR 885.0 274.1 F13B HUMAN 0.647

GVTGYFTFNLYLK 508.3 260.2 PSG5 HUMAN 0.647

SLDFTELDVAAEK 719.4 316.2 ANGT HUMAN 0.647

VVLSSGSGPGLDLPLVLGLPLQLK 791.5 598.4 SHBG HUMAN 0.647

YEFLNGR 449.7 293.1 PLMN HUMAN 0.647

AQPVQVAEGSEPDGFWEALGGK 758.0 623.4 GELS HUMAN 0.644

FLNWIK 410.7 561.3 HABP2 HUMAN 0.644

IAPQLSTEELVSLGEK 857.5 533.3 AFAM HUMAN 0.644

NTVISVNPSTK 580.3 732.4 VCAM1 HUMAN 0.644

SFEGLGQLEVLTLDHNQLQEVK 833.1 503.3 ALS HUMAN 0.644

TFLTVYWTPER 706.9 502.3 ICAM1 HUMAN 0.644

AGFAGDDAPR 488.7 701.3 ACTB HUMAN 0.641

AIGLPEELIQK 605.86 355.2 FABPL HUMAN 0.641

DISEVVTPR 508.3 472.3 CFAB HUMAN 0.641

DPTFIPAPIQAK 433.2 556.3 ANGT HUMAN 0.641

ENPAVIDFELAPIVDLVR 670.7 811.5 C06 HUMAN 0.641

FAFNLYR 465.8 565.3 HEP2 HUMAN 0.641

IAPQLSTEELVSLGEK 857.5 333.2 AFAM HUMAN 0.641

TNTNEFLIDVDK 704.85 849.5 TF HUMAN 0.639

DVLLLVHNLPQNLTGHIWYK 791.8 883.0 PSG7 HUMAN 0.638

LDGSTHLNIFFAK 488.3 739.4 PAPP1 HUMAN 0.638

LPDTPQGLLGEAR 683.87 940.5 EGLN HUMAN 0.638

VVLSSGSGPGLDLPLVLGLPLQLK 791.5 768.5 SHBG HUMAN 0.638

ALALPPLGLAPLLNLWAKPQGR 770.5 457.3 SHBG HUMAN 0.635

LPNNVLQEK 527.8 844.5 AFAM HUMAN 0.635

QINSYVK 426.2 496.3 CBG HUMAN 0.635

QINSYVK 426.2 610.3 CBG HUMAN 0.635

TGAQELLR 444.3 658.4 GELS HUMAN 0.635

TLEAQLTPR 514.8 685.4 HEP2 HUMAN 0.635

WILTAAHTLYPK 471.9 621.4 C1R HUMAN 0.635

SEPRPGVLLR 375.2 454.3 FA7 HUMAN 0.632

AGFAGDDAPR 488.7 630.3 ACTB HUMAN 0.632

DFNQFSSGEK 386.8 333.2 FETA HUMAN 0.632

DVLLLVHNLPQNLTGHIWYK 791.8 310.2 PSG7 HUMAN 0.632

NKPGVYTDVAYYLAWIR 677.0 545.3 FA 12 HUMAN 0.632

SEYGAALAWEK 612.8 788.4 C06 HUMAN 0.632

YNSQLLSFVR 613.8 734.5 TFR1 HUMAN 0.632

ALVLELAK 428.8 672.4 INHBE HUMAN 0.630

ENPAVIDFELAPIVDLVR 670.7 601.4 C06 HUMAN 0.630 Transition Protein AUC

NNQLVAGYLQGPNVNLEEK 700.7 999.5 IL1RA HUMAN 0.630

WGAAPYPv 410.7 577.3 PGRP2 HUMAN 0.630

HELTDEELQSLFTNFANWDK 817.1 854.4 AFAM HUMAN 0.627

Ai PALEDLR 506.8 288.2 APOA1 HUMAN 0.624

AVLHIGEK 289.5 348.7 THBG HUMAN 0.624

EDTPNSVWEPAK 686.8 630.3 CIS HUMAN 0.624

SPELQAEAK 486.8 788.4 APOA2 HUMAN 0.624

YENYTSSFFIR 713.8 756.4 IL12B HUMAN 0.624

NEIVFPAGILQAPFYTR 968.5 456.2 ECE1 HUMAN 0.621

TAVTANLDIR 537.3 288.2 CHL1 HUMAN 0.621

WWGGQPLWITATK 772.4 373.2 ENPP2 HUMAN 0.621

AVDIPGLEAATPYR 736.9 399.2 TENA HUMAN 0.618

ALNFGGIGVVVGHELTHAFDDQGR 837.1 360.2 ECE1 HUMAN 0.618

ALNHLPLEYNSALYSR 621.0 696.4 C06 HUMAN 0.618

FNAVLTNPQGDYDTSTGK 964.5 262.1 C1QC HUMAN 0.618

GDTYPAELYITGSILR 885.0 922.5 F13B HUMAN 0.618

IAQYYYTFK 598.8 884.4 F13B HUMAN 0.618

LEQGENVFLQATDK 796.4 822.4 C1QB HUMAN 0.618

LSITGTYDLK 555.8 696.4 A1AT HUMAN 0.618

NTVISVNPSTK 580.3 845.5 VCAM1 HUMAN 0.618

TLAFVR 353.7 492.3 FA7 HUMAN 0.618

TLEAQLTPR 514.8 814.4 HEP2 HUMAN 0.618

TQIDSPLSGK 523.3 703.4 VCAM1 HUMAN 0.618

AVLHIGEK 289.5 292.2 THBG HUMAN 0.615

FLIPNASQAESK 652.8 931.4 1433Z HUMAN 0.615

FNAVLTNPQGDYDTSTGK 964.5 333.2 C1QC HUMAN 0.615

FQSVFTVTR 542.8 722.4 C1QC HUMAN 0.615

INPASLDK 429.2 630.4 CI 63 A HUMAN 0.615

IPKPEASFSPR 410.2 506.3 ITIH4 HUMAN 0.615

ITQDAQLK 458.8 803.4 CBG HUMAN 0.615

TSYQVYSK 488.2 397.2 CI 63 A HUMAN 0.615

WGAAPYR 410.7 634.3 PGRP2 HUMAN 0.615

AVDIPGLEAATPYR 736.9 286.1 TENA HUMAN 0.613

DVLLLVHNLPQNLPGYFWYK 810.4 328.2 PSG9 HUMAN 0.613

SFEGLGQLEVLTLDHNQLQEVK 833.1 662.8 ALS HUMAN 0.613

TASDFITK 441.7 710.4 GELS HUMAN 0.613

AGPLQAR 356.7 584.4 DEF4 HUMAN 0.610

DYWSTVK 449.7 347.2 APOC3 HUMAN 0.610

FQSVFTVTR 542.79 623.4 C1QC HUMAN 0.610

FQSVFTVTR 542.79 722.4 C1QC HUMAN 0.610

SYTITGLQPGTDYK 772.4 352.2 FINC HUMAN 0.610

FQLSETNR 497.8 476.3 PSG2 HUMAN 0.607

IPKPEASFSPR 410.2 359.2 ITIH4 HUMAN 0.607

LIEIANHVDK 384.6 498.3 ADA 12 HUMAN 0.607

SILFLGK 389.2 201.1 THBG HUMAN 0.607

SLLQPNK 400.2 358.2 C08A HUMAN 0.607

VFQFLEK 455.8 811.4 C05 HUMAN 0.607 Transition Protein AUC

VPGLYYFTYHASSR 554.3 720.3 C1QB HUMAN 0.607

VSAPSGTGHLPGLNPL 506.3 860.5 PSG3 HUMAN 0.607

AGITIPPv 364.2 486.3 IL17 HUMAN 0.604

FLIPNASQAESK 652.8 261.2 1433Z HUMAN 0.604

FQSVFTVTR 542.8 623.4 C1QC HUMAN 0.604

IPvPFFPQQ 516.79 661.4 FIBB HUMAN 0.604

LLELTGP 435.8 644.4 A1BG HUMAN 0.604

SETEIHQGFQHLHQLFAK 717.4 318.1 CBG HUMAN 0.604

SILFLGK 389.2 577.4 THBG HUMAN 0.604

STLFVPPv 410.2 518.3 PEPD HUMAN 0.604

TEQAAVAR 423.2 487.3 FA 12 HUMAN 0.604

EDTPNSVWEPAK 686.8 315.2 CIS HUMAN 0.601

FLNWIK 410.7 560.3 HABP2 HUMAN 0.601

ITQDAQLK 458.8 702.4 CBG HUMAN 0.601

SPELQAEAK 486.8 659.4 APOA2 HUMAN 0.601

TLLPVSKPEIR 418.3 288.2 C05 HUMAN 0.601

VFQFLEK 455.8 276.2 C05 HUMAN 0.601

YGLVTYATYPK 638.3 843.4 CFAB HUMAN 0.601

[00174] Table 14. Univariate AUC values early-middle combined windows

Transition Protein AUC

LDFHFSSDR 375.2 611.3 INHBC HUMAN 0.809

ETLLQDFR 51 1.3 565.3 AMBP HUMAN 0.802

HHGPTITAK 321.2 275.1 AMBP HUMAN 0.801

ATVVYQGER 51 1.8 652.3 APOH HUMAN 0.799

ETLLQDFR 51 1.3 322.2 AMBP HUMAN 0.796

ATVVYQGER 51 1.8 7 1.4 APOH HUMAN 0.795

HHGPTITAK 321.2 432.3 AMBP HUMAN 0.794

TVQAVLTVPK 528.3 855.5 PEDF HUMAN 0.791

AHYDLR 387.7 566.3 FETUA HUMAN 0.789

TVQAVLTVPK 528.3 428.3 PEDF HUMAN 0.787

FICPLTGLWPINTLK 887.0 685.4 APOH HUMAN 0.785

VNHVTLSQPK 374.9 244.2 B2MG HUMAN 0.783

AHYDLR 387.7 288.2 FETUA HUMAN 0.781

ELIEELVNITQNQK 557.6 618.3 IL13 HUMAN 0.780

FSVVYAK 407.2 381.2 FETUA HUMAN 0.777

IQTHSTTYR 369.5 627.3 F13B HUMAN 0.777

DTDTGALLFIGK 625.8 818.5 PEDF HUMAN 0.774

FICPLTGLWPINTLK 887.0 756.9 APOH HUMAN 0.773

DTDTGALLFIGK 625.8 217.1 PEDF HUMAN 0.771

FSVVYAK 407.2 579.4 FETUA HUMAN 0.770

IQTHSTTYR 369.5 540.3 F13B HUMAN 0.769

LDFHFSSDR 375.2 464.2 INHBC HUMAN 0.769

TLAFVR 353.7 274.2 FA7 HUMAN 0.769

FLYHK 354.2 447.2 AMBP HUMAN 0.766 Transition Protein AUC

VNHVTLSQPK 374.9 459.3 B2MG HUMAN 0.762

AIGLPEELIQK 605.86 856.5 FABPL HUMAN 0.752

FLYHK 354.2 284.2 AMBP HUMAN 0.752

ELIEELV ITQNQ 557.6 517.3 IL13 HUMAN 0.751

ETPEGAEAKPWYEPIYLGGVFQLEK 951.14 877.5 TNFA HUMAN 0.751

HFQNLGK 422.2 527.2 AFAM HUMAN 0.749

LIQDAVTGLTVNGQITGDK 972.0 640.4 ITIH3 HUMAN 0.749

LIQDAVTGLTVNGQITGDK 972.0 798.4 ITIH3 HUMAN 0.747

IAPQLSTEELVSLGE 857.5 533.3 AFAM HUMAN 0.745

HFQNLGK 422.2 285.1 AFAM HUMAN 0.740

NNQLVAGYLQGPNVNLEEK 700.7 999.5 ILIRA HUMAN 0.738

VVESLAK 373.2 646.4 IBP1 HUMAN 0.738

IAPQLSTEELVSLGEK 857.5 333.2 AFAM HUMAN 0.737

IALGGLLFPASNLPv 481.3 657.4 SHBG HUMAN 0.734

ALALPPLGLAPLLNLWAKPQGR 770.5 256.2 SHBG HUMAN 0.731

ELPQSIVYK 538.8 417.7 FBLN3 HUMAN 0.724

TFLTVYWTPER 706.9 401.2 ICAM1 HUMAN 0.723

GVTGYFTFNLYLK 508.3 260.2 PSG5 HUMAN 0.717

DVLLLVHNLPQNLTGHIWYK 791.8 310.2 PSG7 HUMAN 0.716

WNFAYWAAHQPWSR 607.3 545.3 PRG2 HUMAN 0.716

YTTEIIK 434.2 603.4 C1R HUMAN 0.716

YTTEIIK 434.2 704.4 C1R HUMAN 0.716

DIPHWLNPTR 416.9 600.3 PAPP1 HUMAN 0.715

WNFAYWAAHQPWSR 607.3 673.3 PRG2 HUMAN 0.715

IALGGLLFPASNLR 481.3 412.3 SHBG HUMAN 0.713

VVLSSGSGPGLDLPLVLGLPLQLK 791.5 598.4 SHBG HUMAN 0.713

GFQALGDAADIR 617.3 717.4 TIMP1 HUMAN 0.711

VVLSSGSGPGLDLPLVLGLPLQLK 791.5 768.5 SHBG HUMAN 0.711

DVLLLVHNLPQNLTGHIWYK 791.8 883.0 PSG7 HUMAN 0.708

YGIEEHGK 311.5 599.3 CXA1 HUMAN 0.706

AEHPTWGDEQLFQTTR 639.3 765.4 PGH1 HUMAN 0.705

VVESLAK 373.2 547.3 IBP1 HUMAN 0.705

DADPDTFFAK 563.8 825.4 AFAM HUMAN 0.704

DAQYAPGYDK 564.3 813.4 CFAB HUMAN 0.704

GFQALGDAADIR 617.3 288.2 TIMP1 HUMAN 0.704

AEHPTWGDEQLFQTTR 639.3 569.3 PGH1 HUMAN 0.702

NFPSPVDAAFR 610.8 959.5 HEMO HUMAN 0.702

ALALPPLGLAPLLNLWAKPQGR 770.5 457.3 SHBG HUMAN 0.701

GVTGYFTFNLYLK 508.3 683.9 PSG5 HUMAN 0.701

DFNQFSSGEK 386.8 189.1 FETA HUMAN 0.699

GDTYPAELYITGSILR 885.0 274.1 F13B HUMAN 0.699

TLEAQLTPR 514.8 685.4 HEP2 HUMAN 0.699

VEHSDLSFSK 383.5 468.2 B2MG HUMAN 0.699

DAQYAPGYDK 564.3 315.1 CFAB HUMAN 0.698

VSEADSSNADWVTK 754.9 347.2 CFAB HUMAN 0.698

ILPSVPK 377.2 244.2 PGH1 HUMAN 0.695

DADPDTFFAK 563.8 302.1 AFAM HUMAN 0.694 Transition Protein AUC

EVFSKPISWEELLQ 852.9 260.2 FA40A HUMAN 0.694

HTLNQIDEVK 598.8 958.5 FETUA HUMAN 0.694

NFPSPVDAAFPv 610.8 775.4 HEMO HUMAN 0.694

VSFSSPLVAISGVALR 802.0 715.4 PAPP1 HUMAN 0.694

TLAFVR 353.7 492.3 FA7 HUMAN 0.693

ILPSVPK 377.2 227.2 PGH1 HUMAN 0.691

LLEVPEGR 456.8 356.2 CIS HUMAN 0.691

TLEAQLTPR 514.8 814.4 HEP2 HUMAN 0.691

IPSNPSHR 303.2 610.3 FBLN3 HUMAN 0.690

LPN VLQEK 527.8 730.4 AFAM HUMAN 0.690

NCSFSIIYPVVIK 770.4 555.4 CRHBP HUMAN 0.690

NCSFSIIYPVVIK 770.4 831.5 CRHBP HUMAN 0.690

VEHSDLSFSK 383.5 234.1 B2MG HUMAN 0.690

ALDLSLK 380.2 185.1 ITIH3 HUMAN 0.688

IHWESASLLR 606.3 437.2 C03 HUMAN 0.688

IPSNPSHR 303.2 496.3 FBLN3 HUMAN 0.688

LDGSTHLNIFFAK 488.3 852.5 PAPP1 HUMAN 0.687

QGHNSVFLIK 381.6 260.2 HEMO HUMAN 0.687

AVLHIGEK 289.5 348.7 THBG HUMAN 0.686

VSEADSSNADWVTK 754.9 533.3 CFAB HUMAN 0.686

TNTNEFLIDVDK 704.85 849.5 TF HUMAN 0.685

AVLHIGEK 289.5 292.2 THBG HUMAN 0.683

HTLNQIDEVK 598.8 951.5 FETUA HUMAN 0.683

VSFSSPLVAISGVALR 802.0 602.4 PAPP1 HUMAN 0.683

IAQYYYTFK 598.8 395.2 F13B HUMAN 0.681

ALDLSLK 380.2 575.3 ITIH3 HUMAN 0.680

LLEVPEGR 456.8 686.4 CIS HUMAN 0.680

QGHNSVFLIK 381.6 520.4 HEMO HUMAN 0.680

SEPRPGVLLR 375.2 454.3 FA7 HUMAN 0.680

SFRPFVPR 335.9 272.2 LBP HUMAN 0.680

AFQVWSDVTPLR 709.88 385.3 MMP2 HUMAN 0.679

FAFNLYR 465.8 712.4 HEP2 HUMAN 0.679

IAQYYYTFK 598.8 884.4 F13B HUMAN 0.679

ITGFLKPGK 320.9 429.3 LBP HUMAN 0.679

EHSSLAFWK 552.8 838.4 APOH HUMAN 0.677

GLQYAAQEGLLALQSELLR 1037.1 858.5 LBP HUMAN 0.676

YYLQGAK 421.7 327.1 ITIH4 HUMAN 0.676

LIENGYFHPVK 439.6 627.4 F13B HUMAN 0.675

SFRPFVPR 335.9 635.3 LBP HUMAN 0.675

AALAAFNAQNNGSNFQLEEISR 789.1 746.4 FETUA HUMAN 0.674

ITGFLKPGK 320.9 301.2 LBP HUMAN 0.673

VQEVLLK 414.8 373.3 HYOU1 HUMAN 0.673

YNSQLLSFVR 613.8 508.3 TFR1 HUMAN 0.673

EHSSLAFWK 552.8 267.1 APOH HUMAN 0.672

FAFNLYR 465.8 565.3 HEP2 HUMAN 0.672

GDTYPAELYITGSILR 885.0 922.5 F13B HUMAN 0.672

ITLPDFTGDLR 624.3 920.5 LBP HUMAN 0.672 Transition Protein AUC

NSDQEIDFK 548.3 409.2 S10A5 HUMAN 0.672

TAVTANLDIR 537.3 802.4 CHL1 HUMAN 0.672

YYLQGAK 421.7 516.3 ITIH4 HUMAN 0.672

ITLPDFTGDLR 624.3 288.2 LBP HUMAN 0.670

AIGLPEELIQK 605.86 355.2 FABPL HUMAN 0.669

ALNFGGIGVVVGHELTHAFDDQGR 837.1 299.2 ECE1 HUMAN 0.668

AQETSGEEISK 589.8 979.5 IBP1 HUMAN 0.668

LPNNVLQEK 527.8 844.5 AFAM HUMAN 0.668

TGISPLALIK 506.8 654.5 APOB HUMAN 0.666

DFHINLFQVLPWLK 885.5 543.3 CFAB HUMAN 0.665

VQEVLLK 414.8 601.4 HYOU1 HUMAN 0.665

YENYTSSFFIR 713.8 756.4 IL12B HUMAN 0.665

CRPINATLAVEK 457.9 559.3 CGB1 HUMAN 0.663

LDGSTHLNIFFAK 488.3 739.4 PAPP1 HUMAN 0.663

TGISPLALIK 506.8 741.5 APOB HUMAN 0.663

EVFSKPISWEELLQ 852.9 376.2 FA40A HUMAN 0.662

SLDFTELDVAAEK 719.4 874.5 ANGT HUMAN 0.662

TFLTVYWTPER 706.9 502.3 ICAM1 HUMAN 0.662

VRPQQLVK 484.3 609.4 ITIH4 HUMAN 0.662

GLQYAAQEGLLALQSELLR 1037.1 929.5 LBP HUMAN 0.661

NAVVQGLEQPHGLWHPLR 688.4 890.6 LRP1 HUMAN 0.661

SILFLGK 389.2 201.1 THBG HUMAN 0.661

DFNQFSSGEK 386.8 333.2 FETA HUMAN 0.659

IHWESASLLR 606.3 251.2 C03 HUMAN 0.659

SILFLGK 389.2 577.4 THBG HUMAN 0.658

SVSLPSLDPASAK 636.4 473.3 APOB HUMAN 0.658

WWGGQPLWITATK 772.4 929.5 ENPP2 HUMAN 0.658

LNIGYIEDLK 589.3 950.5 PAI2 HUMAN 0.657

DFHINLFQVLPWLK 885.5 400.2 CFAB HUMAN 0.657

YSHYNER 323.48 418.2 HABP2 HUMAN 0.657

STLFVPR 410.2 272.2 PEPD HUMAN 0.656

AFQVWSDVTPLR 709.88 347.2 MMP2 HUMAN 0.655

FQSVFTVTR 542.8 722.4 C1QC HUMAN 0.655

GPGEDFR 389.2 623.3 PTGDS HUMAN 0.655

LEEHYELR 363.5 288.2 PAI2 HUMAN 0.655

LPDTPQGLLGEAR 683.87 427.2 EGLN HUMAN 0.655

FQSVFTVTR 542.79 722.4 C1QC HUMAN 0.654

FTFTLHLETPKPSISSSNLNPR 829.4 787.4 PSG1 HUMAN 0.654

NHYTESISVAK 624.8 252.1 NEUR1 HUMAN 0.654

YSHYNER 323.48 581.3 HABP2 HUMAN 0.654

FQSVFTVTR 542.79 623.4 C1QC HUMAN 0.652

IEGNLIFDPNNYLPK 874.0 845.5 APOB HUMAN 0.652

VRPQQLVK 484.3 722.4 ITIH4 HUMAN 0.652

WILTAAHTLYPK 471.9 621.4 C1R HUMAN 0.652

ITQDAQLK 458.8 803.4 CBG HUMAN 0.651

SVSLPSLDPASAK 636.4 885.5 APOB HUMAN 0.651

ESDTSYVSLK 564.8 347.2 CRP HUMAN 0.650 Transition Protein AUC

ESDTSYVSLK 564.8 696.4 CRP HUMAN 0.650

FQSVFTVTR 542.8 623.4 C1QC HUMAN 0.650

HELTDEELQSLFTNFANWDK 817.1 854.4 AFAM HUMAN 0.650

IEGNLIFDPNNYLPK 874.0 414.2 APOB HUMAN 0.650

DIIKPDPPK 511.8 342.2 IL12B HUMAN 0.648

SPELQAEAK 486.8 788.4 APOA2 HUMAN 0.648

VELAPLPSWQPVGK 760.9 400.3 ICAM1 HUMAN 0.648

AQETSGEEISK 589.8 850.4 IBP1 HUMAN 0.647

QTLSWTVTPK 580.8 545.3 PZP HUMAN 0.647

DISEVVTPPv 508.3 787.4 CFAB HUMAN 0.645

DVLLLVHNLPQNLPGYFWYK 810.4 328.2 PSG9 HUMAN 0.645

QTLSWTVTPK 580.8 818.4 PZP HUMAN 0.645

S GAQ AT WTELP WPHEK 613.3 510.3 HEMO HUMAN 0.645

SLDFTELDVAAEK 719.4 316.2 ANGT HUMAN 0.645

AVGYLITGYQR 620.8 523.3 PZP HUMAN 0.644

DISEVVTPR 508.3 472.3 CFAB HUMAN 0.644

FLNWIK 410.7 560.3 HABP2 HUMAN 0.644

IQHPFTVEEFVLPK 562.0 861.5 PZP HUMAN 0.644

ALQDQLVLVAAK 634.9 289.2 ANGT HUMAN 0.643

AVGYLITGYQR 620.8 737.4 PZP HUMAN 0.643

FLNWIK 410.7 561.3 HABP2 HUMAN 0.643

LEQGENVFLQATDK 796.4 822.4 C1QB HUMAN 0.643

LSITGTYDLK 555.8 797.4 A1AT HUMAN 0.641

SEPRPGVLLR 375.2 654.4 FA7 HUMAN 0.641

VPGLYYFTYHASSR 554.3 720.3 C1QB HUMAN 0.641

APLTKPLK 289.9 357.2 CRP HUMAN 0.639

FNAVLTNPQGDYDTSTGK 964.5 333.2 C1QC HUMAN 0.639

IQHPFTVEEFVLPK 562.0 603.4 PZP HUMAN 0.639

LSSPAVITDK 515.8 743.4 PLMN HUMAN 0.639

ALNFGGIGVVVGHELTHAFDDQGR 837.1 360.2 ECE1 HUMAN 0.637

FNAVLTNPQGDYDTSTGK 964.5 262.1 C1QC HUMAN 0.637

LLELTGPK 435.8 227.2 A1BG HUMAN 0.637

YNSQLLSFVR 613.8 734.5 TFR1 HUMAN 0.636

DLYHYITSYVVDGEIIIYGPAYSGR 955.5 707.3 PSG1 HUMAN 0.634

GPGEDFR 389.2 322.2 PTGDS HUMAN 0.634

IHPSYTNYR 575.8 813.4 PSG2 HUMAN 0.634

S GAQ AT WTELP WPHEK 613.3 793.4 HEMO HUMAN 0.634

SPELQAEAK 486.8 659.4 APOA2 HUMAN 0.634

ALQDQLVLVAAK 634.9 956.6 ANGT HUMAN 0.633

ITENDIQIALDDAK 779.9 632.3 APOB HUMAN 0.632

ITQDAQLK 458.8 702.4 CBG HUMAN 0.632

LSSPAVITDK 515.8 830.5 PLMN HUMAN 0.632

SLLQPNK 400.2 358.2 C08A HUMAN 0.632

VPGLYYFTYHASSR 554.3 420.2 C1QB HUMAN 0.632

YGLVTYATYPK 638.3 843.4 CFAB HUMAN 0.632

AGITIPR 364.2 486.3 IL17 HUMAN 0.630

IHPSYTNYR 575.8 598.3 PSG2 HUMAN 0.630 Transition Protein AUC

QINSYVK 426.2 610.3 CBG HUMAN 0.630

S SNNPHSPIVEEFQVPYNK 729.4 261.2 CIS HUMAN 0.630

ANDQYLTAAALHNLDEAVK 686.3 317.2 ILIA HUMAN 0.629

ATWSGAVLAGR 544.8 730.4 A1BG HUMAN 0.629

TLPFSR 360.7 506.3 LYAM1 HUMAN 0.629

TYLHTYESEI 628.3 515.3 ENPP2 HUMAN 0.629

EFDDDTYDNDIALLQLK 1014.48 388.3 TPA HUMAN 0.627

EFDDDTYDNDIALLQLK 1014.48 501.3 TPA HUMAN 0.627

VTGLDFIPGLHPILTLSK 641.04 771.5 LEP HUMAN 0.627

HVVQLR 376.2 614.4 IL6RA HUMAN 0.626

LIENGYFHPVK 439.6 343.2 F13B HUMAN 0.626

LLELTGP 435.8 644.4 A1BG HUMAN 0.626

YEVQGEVFTKPQLWP 911.0 392.2 CRP HUMAN 0.626

DPNGLPPEAQK 583.3 497.2 RET4 HUMAN 0.625

FTFTLHLETPKPSISSSNLNPR 829.4 874.4 PSG1 HUMAN 0.625

YGLVTYATYP 638.3 334.2 CFAB HUMAN 0.625

APLTKPLK 289.9 398.8 CRP HUMAN 0.623

DSPSVWAAVPG 607.31 301.2 PROF1 HUMAN 0.623

ENPAVIDFELAPIVDLVR 670.7 811.5 C06 HUMAN 0.623

ILILPSVTR 506.3 559.3 PSGx HUMAN 0.623

SFEGLGQLEVLTLDHNQLQEVK 833.1 503.3 ALS HUMAN 0.623

TSESGELHGLTTEEEFVEGIYK 819.06 310.2 TTHY HUMAN 0.623

AGITIPR 364.2 272.2 IL17 HUMAN 0.622

DPDQTDGLGLSYLSSHIANVER 796.4 328.1 GELS HUMAN 0.622

ATWSGAVLAGR 544.8 643.4 A1BG HUMAN 0.620

HVVQLR 376.2 515.3 IL6RA HUMAN 0.620

QINSYVK 426.2 496.3 CBG HUMAN 0.620

TLFIFGVTK 513.3 215.1 PSG4 HUMAN 0.620

YEVQGEVFTKPQLWP 911.0 293.1 CRP HUMAN 0.620

YYGYTGAFR 549.3 771.4 TRFL HUMAN 0.620

AALAAFNAQNNGSNFQLEEISR 789.1 633.3 FETUA HUMAN 0.619

ALNHLPLEYNSALYSR 621.0 696.4 C06 HUMAN 0.619

EDTPNSVWEPAK 686.8 630.3 CIS HUMAN 0.619

NNQLVAGYLQGPNVNLEEK 700.7 357.2 IL1RA HUMAN 0.619

ELANTIK 394.7 475.3 S10AC HUMAN 0.618

ENPAVIDFELAPIVDLVR 670.7 601.4 C06 HUMAN 0.618

GEVTYTTSQVSK 650.3 913.5 EGLN HUMAN 0.616

NEIWYR 440.7 637.4 FA 12 HUMAN 0.616

TLFIFGVTK 513.3 811.5 PSG4 HUMAN 0.616

DLYHYITSYVVDGEIIIYGPAYSGR 955.5 650.3 PSG1 HUMAN 0.615

DPTFIPAPIQAK 433.2 556.3 ANGT HUMAN 0.615

VELAPLPSWQPVGK 760.9 342.2 ICAM1 HUMAN 0.615

DPNGLPPEAQK 583.3 669.4 RET4 HUMAN 0.614

GIVEECCFR 585.3 900.3 IGF2 HUMAN 0.614

ITENDIQIALDDAK 779.9 873.5 APOB HUMAN 0.614

LSETNR 360.2 330.2 PSG1 HUMAN 0.614

L SNENHGI AQR 413.5 519.8 ITIH2 HUMAN 0.614 Transition Protein AUC

YEFLNGR 449.7 293.1 PLMN HUMAN 0.614

AEIEYLEK 497.8 552.3 LYAM1 HUMAN 0.612

GIVEECCFR 585.3 771.3 IGF2 HUMAN 0.612

ILDDLSPR 464.8 587.3 ITIH4 HUMAN 0.611

IRPHTFTGLSGLR 485.6 545.3 ALS HUMAN 0.611

VVGGLVALR 442.3 784.5 FA 12 HUMAN 0.609

LEEHYELR 363.5 417.2 PAI2 HUMAN 0.609

L SNENHGI AQR 413.5 544.3 ITIH2 HUMAN 0.609

TYLHTYESEI 628.3 908.4 ENPP2 HUMAN 0.609

VLEPTLK 400.3 587.3 VTDB HUMAN 0.609

ILILPSVTR 506.3 785.5 PSGx HUMAN 0.608

TAVTANLDIR 537.3 288.2 CHL1 HUMAN 0.608

WWGGQPLWITATK 772.4 373.2 ENPP2 HUMAN 0.607

ALVLELAK 428.8 672.4 INHBE HUMAN 0.605

EAQLPVIENK 570.8 329.2 PLMN HUMAN 0.605

QRPPDLDTSSNAVDLLFFTDESGDSR 961.5 866.3 C1R HUMAN 0.605

TDAPDLPEENQAR 728.3 613.3 C05 HUMAN 0.605

TLPFSR 360.7 409.2 LYAM1 HUMAN 0.605

VQTAHFK 277.5 502.3 C08A HUMAN 0.605

ANLFNNIFELAGLGK 793.9 299.2 LCAP HUMAN 0.604

FQLPGQK 409.2 275.1 PSG1 HUMAN 0.604

NTVISVNPSTK 580.3 845.5 VCAM1 HUMAN 0.604

VLEPTLK 400.3 458.3 VTDB HUMAN 0.604

YWGVASFLQK 599.8 849.5 RET4 HUMAN 0.604

AGPLQAR 356.7 584.4 DEF4 HUMAN 0.602

AHQLAIDTYQEFEETYIPK 766.0 521.3 CSH HUMAN 0.602

DLHLSDVFLK 396.2 366.2 C06 HUMAN 0.602

SSNNPHSPIVEEFQVPYNK 729.4 521.3 CIS HUMAN 0.602

YWGVASFLQK 599.8 350.2 RET4 HUMAN 0.602

AGPLQAR 356.7 487.3 DEF4 HUMAN 0.601

ALNHLPLEYNSALYSR 621.0 538.3 C06 HUMAN 0.601

EAQLPVIENK 570.8 699.4 PLMN HUMAN 0.601

EDTPNSVWEPAK 686.8 315.2 CIS HUMAN 0.601

NTVISVNPSTK 580.3 732.4 VCAM1 HUMAN 0.601

[00175] Table 15. Univariate AUC values middle-late combined windows

Transition Protein AUC

TVQAVLTVPK 528.3 855.5 PEDF HUMAN 0.7604

GDTYPAELYITGSILR 885.0 922.5 F13B HUMAN 0.7604

DVLLLVHNLPQNLTGHIWYK 791.8 883.0 PSG7 HUMAN 0.7604

TLPFSPv 360.7 506.3 LYAM1 HUMAN 0.7563

ALALPPLGLAPLLNLWAKPQGR 770.5 457.3 SHBG HUMAN 0.7563

IALGGLLFPASNLR 481.3 657.4 SHBG HUMAN 0.7542

IALGGLLFPASNLR 481.3 412.3 SHBG HUMAN 0.7542

DTDTGALLFIGK 625.8 217.1 PEDF HUMAN 0.7500

QGFGNVATNTDGK 654.81 706.3 FIBB HUMAN 0.7438

ETLLQDFR 511.3 565.3 AMBP HUMAN 0.7438

ETLLQDFR 511.3 322.2 AMBP HUMAN 0.7417

IAQYYYTFK 598.8 884.4 F13B HUMAN 0.7396

DTDTGALLFIGK 625.8 818.5 PEDF HUMAN 0.7396

AEIEYLEK 497.8 552.3 LYAM1 HUMAN 0.7396

LDFHFSSDR 375.2 611.3 INHBC HUMAN 0.7354

YQISVNK 426.2 560.3 FIBB HUMAN 0.7333

IAPQLSTEELVSLGEK 857.5 533.3 AFAM HUMAN 0.7313

EVFSKPISWEELLQ 852.9 376.2 FA40A HUMAN 0.7292

TLAFVR 353.7 274.2 FA7 HUMAN 0.7229

HHGPTITAK 321.2 275.1 AMBP HUMAN 0.7229

SLQAFVAVAAR 566.8 487.3 IL23A HUMAN 0.7208

IAQYYYTFK 598.8 395.2 F13B HUMAN 0.7208

EVFSKPISWEELLQ 852.9 260.2 FA40A HUMAN 0.7208

DPNGLPPEAQK 583.3 669.4 RET4 HUMAN 0.7208

DPNGLPPEAQK 583.3 497.2 RET4 HUMAN 0.7167

VEHSDLSFSK 383.5 468.2 B2MG HUMAN 0.7146

YQISVNK 426.2 292.1 FIBB HUMAN 0.7125

TLAFVR 353.7 492.3 FA7 HUMAN 0.7125

IAPQLSTEELVSLGEK 857.5 333.2 AFAM HUMAN 0.7125

AEIEYLEK 497.8 389.2 LYAM1 HUMAN 0.7125

YWGVASFLQK 599.8 849.5 RET4 HUMAN 0.7104

TLPFSR 360.7 409.2 LYAM1 HUMAN 0.7104

HFQNLGK 422.2 527.2 AFAM HUMAN 0.7104

TQILEWAAER 608.8 761.4 EGLN HUMAN 0.7083

HFQNLGK 422.2 285.1 AFAM HUMAN 0.7063

FTFTLHLETPKPSISSSNLNPR 829.4 787.4 PSG1 HUMAN 0.7063

DPDQTDGLGLSYLSSHIANVER 796.4 456.2 GELS HUMAN 0.7063

DADPDTFFAK 563.8 825.4 AFAM HUMAN 0.7042

YWGVASFLQK 599.8 350.2 RET4 HUMAN 0.7021

DADPDTFFAK 563.8 302.1 AFAM HUMAN 0.7021

HHGPTITAK 321.2 432.3 AMBP HUMAN 0.6979

NTVISVNPSTK 580.3 845.5 VCAM1 HUMAN 0.6958

FLYHK 354.2 447.2 AMBP HUMAN 0.6958

FICPLTGLWPINTLK 887.0 685.4 APOH HUMAN 0.6958

FTFTLHLETPKPSISSSNLNPR 829.4 874.4 PSG1 HUMAN 0.6938

FLYHK 354.2 284.2 AMBP HUMAN 0.6938

EALVPLVADHK 397.9 390.2 HGFA HUMAN 0.6938 Transition Protein AUC

LNIGYIEDLK 589.3 837.4 PAI2 HUMAN 0.6917

QGFGNVATNTDGK 654.81 319.2 FIBB HUMAN 0.6896

EALVPLVADHK 397.9 439.8 HGFA HUMAN 0.6896

TNTNEFLIDVDK 704.85 849.5 TF HUMAN 0.6875

DTYVSSFPR 357.8 272.2 TCEA1 HUMAN 0.6813

VNHVTLSQPK 374.9 244.2 B2MG HUMAN 0.6771

GPGEDFR 389.2 623.3 PTGDS HUMAN 0.6771

GEVTYTTSQVSK 650.3 913.5 EGLN HUMAN 0.6771

GEVTYTTSQVSK 650.3 750.4 EGLN HUMAN 0.6771

FICPLTGLWPINTLK 887.0 756.9 APOH HUMAN 0.6771

YEFLNGR 449.7 606.3 PLMN HUMAN 0.6750

YEFLNGR 449.7 293.1 PLMN HUMAN 0.6750

TLFIFGVTK 513.3 215.1 PSG4 HUMAN 0.6750

LNIGYIEDLK 589.3 950.5 PAI2 HUMAN 0.6750

LLELTGPK 435.8 227.2 A1BG HUMAN 0.6750

TPSAAYLWVGTGASEAEK 919.5 849.4 GELS HUMAN 0.6729

FQLPGQK 409.2 275.1 PSG1 HUMAN 0.6729

ELIEELVNITQNQK 557.6 618.3 IL13 HUMAN 0.6729

DLYHYITSYVVDGEIIIYGPAYSGR 955.5 707.3 PSG1 HUMAN 0.6729

AHYDLR 387.7 566.3 FETUA HUMAN 0.6729

LLEVPEGR 456.8 356.2 CIS HUMAN 0.6708

TLFIFGVTK 513.3 811.5 PSG4 HUMAN 0.6688

FQLPGQK 409.2 429.2 PSG1 HUMAN 0.6667

DLYHYITSYVVDGEIIIYGPAYSGR 955.5 650.3 PSG1 HUMAN 0.6667

YYLQGAK 421.7 516.3 ITIH4 HUMAN 0.6646

FSVVYAK 407.2 579.4 FETUA HUMAN 0.6646

EQLGEFYEALDCLR 871.9 747.4 A1AG1 HUMAN 0.6646

LDFHFSSDR 375.2 464.2 INHBC HUMAN 0.6625

ALNHLPLEYNSALYSR 621.0 696.4 C06 HUMAN 0.6625

YYLQGAK 421.7 327.1 ITIH4 HUMAN 0.6604

YTTEIIK 434.2 704.4 C1R HUMAN 0.6604

VEHSDLSFSK 383.5 234.1 B2MG HUMAN 0.6604

SNPVTLNVLYGPDLPR 585.7 654.4 PSG6 HUMAN 0.6604

LWAYLTIQELLAK 781.5 300.2 ITIH1 HUMAN 0.6604

FSLVSGWGQLLDR 493.3 403.2 FA7 HUMAN 0.6604

ATVVYQGER 511.8 652.3 APOH HUMAN 0.6604

TPSAAYLWVGTGASEAEK 919.5 428.2 GELS HUMAN 0.6583

SEPRPGVLLR 375.2 454.3 FA7 HUMAN 0.6583

LSSPAVITDK 515.8 830.5 PLMN HUMAN 0.6583

GPGEDFR 389.2 322.2 PTGDS HUMAN 0.6583

EFDDDTYDNDIALLQLK 1014.48 501.3 TPA HUMAN 0.6583

TFLTVYWTPER 706.9 502.3 ICAM1 HUMAN 0.6563

NTVISVNPSTK 580.3 732.4 VCAM1 HUMAN 0.6563

LPNNVLQEK 527.8 730.4 AFAM HUMAN 0.6563

LPDTPQGLLGEAR 683.87 427.2 EGLN HUMAN 0.6563

VANYVDWINDR 682.8 818.4 HGFA HUMAN 0.6542

LSSPAVITDK 515.8 743.4 PLMN HUMAN 0.6542 Transition Protein AUC

LPN VLQEK 527.8 844.5 AFAM HUMAN 0.6542

IPGIFELGIS SQ SDR 809.9 849.4 C08B HUMAN 0.6542

GAVHWVAETDYQSFAVLYLER 822.8 580.3 C08G HUMAN 0.6542

FLNWI 410.7 560.3 HABP2 HUMAN 0.6542

TFLTVYWTPER 706.9 401.2 ICAM1 HUMAN 0.6521

Ni PGVYTDVAYYLAWIR 677.0 821.5 FA 12 HUMAN 0.6521

AHYDLR 387.7 288.2 FETUA HUMAN 0.6521

LLEVPEGR 456.8 686.4 CIS HUMAN 0.6500

LIENGYFHPVK 439.6 627.4 F13B HUMAN 0.6500

GFQALGDAADIR 617.3 717.4 TIMP1 HUMAN 0.6500

ELIEELV ITQNQ 557.6 517.3 IL13 HUMAN 0.6500

EAQLPVIENK 570.8 329.2 PLMN HUMAN 0.6479

CRPINATLAVEK 457.9 559.3 CGB1 HUMAN 0.6479

ATVVYQGER 511.8 751.4 APOH HUMAN 0.6479

ALNHLPLEY SALYSR 621.0 538.3 C06 HUMAN 0.6479

AHQLAIDTYQEFEETYIPK 766.0 634.4 CSH HUMAN 0.6479

VTGLDFIPGLHPILTLSK 641.04 771.5 LEP HUMAN 0.6458

VANYVDWINDR 682.8 917.4 HGFA HUMAN 0.6458

SSNNPHSPIVEEFQVPYNK 729.4 261.2 CIS HUMAN 0.6458

NiCPGVYTDVAYYLAWIR 677.0 545.3 FA 12 HUMAN 0.6458

GSLVQASEANLQAAQDFVR 668.7 735.4 ITIH1 HUMAN 0.6458

YTTEIIK 434.2 603.4 C1R HUMAN 0.6438

NEIVFPAGILQAPFYTR 968.5 357.2 ECE1 HUMAN 0.6438

IPGIFELGIS SQ SDR 809.9 679.3 C08B HUMAN 0.6438

SNPVTLNVLYGPDLPR 585.7 817.4 PSG6 HUMAN 0.6417

LLELTGP 435.8 644.4 A1BG HUMAN 0.6417

EAQLPVIENK 570.8 699.4 PLMN HUMAN 0.6417

AEHPTWGDEQLFQTTR 639.3 765.4 PGH1 HUMAN 0.6417

YGIEEHGK 311.5 599.3 CXA1 HUMAN 0.6396

TQIDSPLSGK 523.3 703.4 VCAM1 HUMAN 0.6396

YHFEALADTGISSEFYDNANDLLSK 940.8 301.1 C08A HUMAN 0.6375

S CDL ALLET YC ATP AK 906.9 315.2 IGF2 HUMAN 0.6375

NAVVQGLEQPHGLWHPLR 688.4 285.2 LRP1 HUMAN 0.6375

HVVQLR 376.2 614.4 IL6RA HUMAN 0.6375

NNQLVAGYLQGPNVNLEEK 700.7 999.5 IL1RA HUMAN 0.6354

GIVEECCFR 585.3 771.3 IGF2 HUMAN 0.6354

DGSPDVTTADIGANTPDATK 973.5 531.3 PGRP2 HUMAN 0.6354

AEHPTWGDEQLFQTTR 639.3 569.3 PGH1 HUMAN 0.6354

YVVISQGLDKPR 458.9 400.3 LRP1 HUMAN 0.6333

WGAAPYR 410.7 577.3 PGRP2 HUMAN 0.6333

VRPQQLVK 484.3 609.4 ITIH4 HUMAN 0.6333

AVYEAVLR 460.8 750.4 PEPD HUMAN 0.6333

TQIDSPLSGK 523.3 816.5 VCAM1 HUMAN 0.6313

IPKPEASFSPR 410.2 359.2 ITIH4 HUMAN 0.6313

HELTDEELQSLFTNFANWDK 817.1 854.4 AFAM HUMAN 0.6313

GSLVQASEANLQAAQDFVR 668.7 806.4 ITIH1 HUMAN 0.6313

GAVHWVAETDYQSFAVLYLER 822.8 863.5 C08G HUMAN 0.6313 Transition Protein AUC

ENPAVIDFELAPIVDLVR 670.7 811.5 C06 HUMAN 0.6313

VRPQQLV 484.3 722.4 ITIH4 HUMAN 0.6292

IRPFFPQQ 516.79 372.2 FIBB HUMAN 0.6292

LWAYLTIQELLAK 781.5 371.2 mm HUMAN 0.6271

EQLGEFYEALDCLPv 871.9 563.3 A1AG1 HUMAN 0.6271

LLDFEFSSGR 585.8 553.3 G6PE HUMAN 0.6250

LIENGYFHPVK 439.6 343.2 F13B HUMAN 0.6250

ENPAVIDFELAPIVDLVR 670.7 601.4 C06 HUMAN 0.6250

WNFAYWAAHQPWSR 607.3 545.3 PRG2 HUMAN 0.6229

TAVTANLDIR 537.3 802.4 CHL1 HUMAN 0.6229

WNFAYWAAHQPWSR 607.3 673.3 PRG2 HUMAN 0.6208

HTLNQIDEVK 598.8 951.5 FETUA HUMAN 0.6208

DPDQTDGLGLSYLSSHIANVER 796.4 328.1 GELS HUMAN 0.6208

WGAAPYR 410.7 634.3 PGRP2 HUMAN 0.6188

TEQAAVAR 423.2 487.3 FA 12 HUMAN 0.6188

LEEHYELR 363.5 288.2 PAI2 HUMAN 0.6188

GIVEECCFR 585.3 900.3 IGF2 HUMAN 0.6188

YHFEALADTGISSEFYDNANDLLSK 940.8 874.5 C08A HUMAN 0.6167

TQILEWAAER 608.8 632.3 EGLN HUMAN 0.6167

DSPSVWAAVPGK 607.31 301.2 PROF1 HUMAN 0.6167

DLHLSDVFLK 396.2 260.2 C06 HUMAN 0.6167

AQPVQVAEGSEPDGFWEALGGK 758.0 574.3 GELS HUMAN 0.6167

YSHYNER 323.48 581.3 HABP2 HUMAN 0.6146

YSHYNER 323.48 418.2 HABP2 HUMAN 0.6146

VNHVTLSQPK 374.9 459.3 B2MG HUMAN 0.6146

EHSSLAFWK 552.8 267.1 APOH HUMAN 0.6146

TATSEYQTFFNPR 781.4 386.2 THRB HUMAN 0.6104

SGFSFGFK 438.7 732.4 C08B HUMAN 0.6104

GFQALGDAADIR 617.3 288.2 TIMP1 HUMAN 0.6104

FSVVYAK 407.2 381.2 FETUA HUMAN 0.6104

QTLSWTVTPK 580.8 545.3 PZP HUMAN 0.6083

QLGLPGPPDVPDHAAYHPF 676.7 263.1 ITIH4 HUMAN 0.6083

LSITGTYDLK 555.8 797.4 A1AT HUMAN 0.6083

LPDTPQGLLGEAR 683.87 940.5 EGLN HUMAN 0.6083

VVESLAK 373.2 646.4 IBP1 HUMAN 0.6063

VSEADSSNADWVTK 754.9 347.2 CFAB HUMAN 0.6063

TEQAAVAR 423.2 615.4 FA 12 HUMAN 0.6063

SEPRPGVLLR 375.2 654.4 FA7 HUMAN 0.6063

QTLSWTVTPK 580.8 818.4 PZP HUMAN 0.6063

HYINLITR 15.3 301.1 NPY HUMAN 0.6063

DPTFIPAPIQAK 433.2 461.2 ANGT HUMAN 0.6063

VSEADSSNADWVTK 754.9 533.3 CFAB HUMAN 0.6042

VQEVLLK 414.8 373.3 HYOU1 HUMAN 0.6042

SILFLGK 389.2 577.4 THBG HUMAN 0.6042

IQHPFTVEEFVLPK 562.0 603.4 PZP HUMAN 0.6042

ELPQSIVYK 538.8 417.7 FBLN3 HUMAN 0.6042

AVGYLITGYQR 620.8 737.4 PZP HUMAN 0.6042 Transition Protein AUC

ATWSGAVLAGR 544.8 643.4 A1BG HUMAN 0.6042

AKPALEDLR 506.8 288.2 APOA1 HUMAN 0.6042

SEYGAALAWEK 612.8 845.5 C06 HUMAN 0.6021

NVNQSLLELHK 432.2 656.3 FRIH HUMAN 0.6021

IQHPFTVEEFVLPK 562.0 861.5 PZP HUMAN 0.6021

IPKPEASFSPR 410.2 506.3 ITIH4 HUMAN 0.6021

GVTGYFTF LYLK 508.3 260.2 PSG5 HUMAN 0.6021

DGSPDVTTADIGANTPDATK 973.5 844.4 PGRP2 HUMAN 0.6021

AVGYLITGYQR 620.8 523.3 PZP HUMAN 0.6021

ANDQYLTAAALHNLDEAVK 686.3 317.2 ILIA HUMAN 0.6021

TLYSSSPR 455.7 696.3 IC1 HUMAN 0.6000

LHKPGVYTR 357.5 479.3 HGFA HUMAN 0.6000

IIGGSDADIK 494.8 260.2 CI S HUMAN 0.6000

HELTDEELQSLFTNFANWDK 817.1 906.5 AFAM HUMAN 0.6000

GGEGTGYFVDFSVR 745.9 869.5 HRG HUMAN 0.6000

AVLHIGEK 289.5 348.7 THBG HUMAN 0.6000

ALVLELAK 428.8 672.4 INHBE HUMAN 0.6000

[00176] Table 16. Lasso Summed Coefficients All Windows

Transition Protein SumBestCoefs Al

1

ELIEELV ITQNQ 557.6 618.3 IL13 HUMAN 2.5088

DLHLSDVFLK 396.2 260.2 C06 HUMAN 2.4010

SYTITGLQPGTDYK 772.4 352.2 FINC HUMAN 2.3304

SPELQAEA _486.8_788.4 APOA2 HUMA 2.2657

N

VNHVTLSQP 374.9 459.3 B2MG HUMAN 2.1480

DTDTGALLFIGK 625.8 818.5 PEDF HUMAN 2.0051

LLDFEFSSGR 585.8 944.4 G6PE HUMAN 1.7763

GPGEDFR 389.2 623.3 PTGDS HUMA 1.6782

N

DPNGLPPEAQK 583.3 669.4 RET4 HUMAN 1.6581

IQTHSTTYR 369.5 540.3 F13B HUMAN 1.6107

VNHVTLSQPK 374.9 244.2 B2MG HUMAN 1.4779

STLFVPR 410.2 518.3 PEPD HUMAN 1.3961

GEVTYTTSQVSK 650.3 913.5 EGLN HUMAN 1.3306

ALVLELAK 428.8 672.4 INHBE HUMAN 1.2973

ANDQYLTAAALHNLDEAVK 686.3 317.2 ILIA HUMAN 1.1850

STLFVPR 410.2 272.2 PEPD HUMAN 1.1842

GPGEDFR 389.2 322.2 PTGDS HUMA 1.1742

N

IPSNPSHR 303.2 610.3 FBLN3 HUMAN 1.0868

HHGPTITAK 321.2 432.3 AMBP HUMAN 1.0813

TLAFVR 353.7 274.2 FA7 HUMAN 1.0674

DLHLSDVFLK 396.2 366.2 C06 HUMAN 0.9887

EFDDDTYDNDIALLQLK 1014.48 501.3 TPA HUMAN 0.9468

AIGLPEELIQK_605.86_856.5 FABPL HUMA 0.7740

N

LIENGYFHPVK 439.6 343.2 F13B HUMAN 0.7740

LPDTPQGLLGEAR 683.87 427.2 EGLN HUMAN 0.6748

EHSSLAFWK 552.8 267.1 APOH HUMAN 0.6035

NCSFSIIYPVVIK_770.4_831.5 CRHBP HUMA 0.6014

N

ALNSIIDVYHK 424.9 661.3 S10A8 HUMAN 0.5987

WGAAPYR 410.7 577.3 PGRP2 HUMAN 0.5699

TQILEWAAER 608.8 632.3 EGLN HUMAN 0.5395

IPSNPSHR 303.2 496.3 FBLN3 HUMAN 0.4845

VEHSDLSFSK 383.5 234.1 B2MG HUMAN 0.4398

VEHSDLSFSK 383.5 468.2 B2MG HUMAN 0.3883

FLYHK 354.2 284.2 AMBP HUMAN 0.3410

LPDTPQGLLGEAR 683.87 940.5 EGLN HUMAN 0.3282

EALVPLVADHK 397.9 390.2 HGFA HUMAN 0.3091 lEGNLlFDPNNYLPK 874.0 845.5 APOB HUMAN 0.2933

LIENGYFHPVK 439.6 627.4 F13B HUMAN 0.2896

VPLALFALNR 557.3 620.4 PEPD HUMAN 0.2875

FICPLTGLWPINTLK 887.0 685.4 APOH HUMAN 0.2823

NAVVQGLEQPHGLWHPLR 688.4 890.6 LRP1 HUMAN 0.2763 Transition Protein SumBestCoefs Al

1

ALNFGGIGVVVGHELTHAFDDQGR 837.1 299 ECE1 HUMAN 0.2385

.2

SPELQAEAK 486.8 659.4 APOA2 HUMA 0.2232

N

EVFSKPISWEELLQ 852.9 260.2 FA40A HUMAN 0.1608

VANYVDWINDR 682.8 917.4 HGFA HUMAN 0.1507

EVFSKPISWEELLQ 852.9 376.2 FA40A HUMAN 0.1487

HVVQLR 376.2 614.4 IL6RA HUMAN 0.1256

TVQAVLTVPK 528.3 855.5 PEDF HUMAN 0.1 170

ELIEELVNITQNQK 557.6 517.3 IL13 HUMAN 0.1 159

EALVPLVADHK 397.9 439.8 HGFA HUMAN 0.0979

AITPPHPASQANIIFDITEGNLR 825.8 917.5 FBLN1 HUMAN 0.0797

FLYHK 354.2 447.2 AMBP HUMAN 0.0778

SLLQPNK 400.2 358.2 C08A HUMAN 0.0698

TGISPLALIK 506.8 654.5 APOB HUMAN 0.0687

ALNFGGIGVVVGHELTHAFDDQGR 837.1 360 ECE1 HUMAN 0.0571

.2

DYWSTVK_449.7_347.2 APOC3 HUMA 0.0357

N

AITPPHPASQANIIFDITEGNLR 825.8 459.3 FBLN1 HUMAN 0.0313

AALAAFNAQNNGSNFQLEEISR 789.1_633.3 FETUA HUMA 0.0279

N

DPNGLPPEAQK 583.3 497.2 RET4 HUMAN 0.0189

TLAFVR 353.7 492.3 FA7 HUMAN 0.0087

[00177] Table 17. Lasso Summed Coefficients Early Window

Transition Protein SumBestCoefs Early

SGFSFGFK 438.7 732.4 C08B HUMAN 1.0459

VVGGLVALR 442.3 784.5 FA12 HUMAN 0.9572

IEGNLIFDPN YLPK 874.0 845.5 APOB HUMAN 0.9571

ETLLQDFR 511.3 565.3 AMBP HUMAN 0.7851

LSIPQITTK 500.8 687.4 PSG5 HUMAN 0.7508

TASDFITK 441.7 710.4 GELS HUMAN 0.6549

YGIEEHGK 311.5 599.3 CXA1 HUMAN 0.6179

AFQVWSDVTPLR 709.88 347.2 MMP2 HUMAN 0.6077

TVQAVLTVPK 528.3 855.5 PEDF HUMAN 0.5889

LSITGTYDL 555.8 696.4 A1AT HUMAN 0.5857

ELIEELV ITQNQ 557.6 517.3 IL13 HUMAN 0.5334

LIENGYFHPVK 439.6 627.4 F13B HUMAN 0.5257

NEIVFPAGILQAPFYTR 968.5 357.2 ECE1 HUMAN 0.4601

SLLQPNK 400.2 358.2 C08A HUMAN 0.4347

LSIPQITTK 500.8 800.5 PSG5 HUMAN 0.4329

GVTGYFTF LYLK 508.3 683.9 PSG5 HUMAN 0.4302

IQTHSTTYR 369.5 627.3 F13B HUMAN 0.4001

ATVVYQGER 511.8 652.3 APOH HUMAN 0.3909

LPDTPQGLLGEAR 683.87 427.2 EGLN HUMAN 0.3275

NNQLVAGYLQGPNVNLEEK 700.7 999.5 IL1RA HUMAN 0.3178

SERPPIFEIR 415.2 564.3 LRP1 HUMAN 0.3112

AHYDLR 387.7 566.3 FETUA HUMAN 0.2900

NEIWYR 440.7 637.4 FA12 HUMAN 0.2881

ALDLSLK 380.2 575.3 ITIH3 HUMAN 0.2631

NKPGVYTDVAYYLAWIR 677.0 545.3 FA12 HUMAN 0.2568

SYTITGLQPGTDYK 772.4 352.2 FINC HUMAN 0.2277

LFIPQITPK 528.8 683.4 PSG11 HUMAN 0.2202

IIGGSDADIK 494.8 260.2 CIS HUMAN 0.2182

AVDIPGLEAATPYR 736.9 399.2 TENA HUMAN 0.2113

DTDTGALLFIGK 625.8 818.5 PEDF HUMAN 0.2071

AEIEYLEK 497.8 389.2 LYAM1 HUMAN 0.1925

EHSSLAFWK 552.8 838.4 APOH HUMAN 0.1899

LPDTPQGLLGEAR 683.87 940.5 EGLN HUMAN 0.1826

WGAAPYR 410.7 577.3 PGRP2 HUMAN 0.1669

LFIPQITPK 528.8 261.2 PSG11 HUMAN 0.1509

WWGGQPLWITATK 772.4 929.5 ENPP2 HUMAN 0.1446

DSPSVWAAVPGK 607.31 301.2 PROF1 HUMAN 0.1425

LIQDAVTGLTVNGQITGDK 972.0 798.4 ITIH3 HUMAN 0.1356

ALDLSLK 380.2 185.1 ITIH3 HUMAN 0.1305

TVQAVLTVPK 528.3 428.3 PEDF HUMAN 0.1249

NAVVQGLEQPHGLWHPLR 688.4 890.6 LRP1 HUMAN 0.1092

NSDQEIDFK 548.3 409.2 S10A5 HUMAN 0.0937

YNSQLLSFVR 613.8 508.3 TFR1 HUMAN 0.0905

LLDFEFSSGR 585.8 553.3 G6PE HUMAN 0.0904

ALNFGGIGVVVGHELTHAFDDQGR 837.1 2 ECE1 HUMAN 0.0766

99.2

STLFVPR 410.2 518.3 PEPD HUMAN 0.0659 Transition Protein SumBestCoefs Early

DLHLSDVFLK 396.2 260.2 C06 HUMAN 0.0506

EHSSLAFWK 552.8 267.1 APOH HUMAN 0.0452

TQIDSPLSGK 523.3 703.4 VCAM1 HUMA 0.0447

N

HHGPTITAK 321.2 432.3 AMBP HUMAN 0.0421

AFQVWSDVTPLR 709.88 385.3 MMP2 HUMAN 0.0417

TGISPLALIK 506.8 741.5 APOB HUMAN 0.0361

DLHLSDVFLK 396.2 366.2 C06 HUMAN 0.0336

NTVISVNPSTK_580.3_845.5 VCAM1 HUMA 0.0293

N

DIIKPDPPK 511.8 342.2 IL12B HUMAN 0.0219

TGISPLALIK 506.8 654.5 APOB HUMAN 0.0170

GAVHWVAETDYQSFAVLYLER 822.8 580. C08G_HUMAN 0.0151

3

LNIGYIEDLK 589.3 837.4 PAI2 HUMAN 0.0048

GPGEDFR 389.2 322.2 PTGDS HUMAN 0.0008

[00178] Table 18. Lasso Summed Coefficients Early Middle Combined Windows

Transition Protein SumBestCoefs EM

AHYDLR 387.7 288.2 FETUA HUMAN 2.1058

NCSFSIIYPVVIK 770.4 831.5 CRHBP HUMAN 2.0427

AIGLPEELIQK 605.86 856.5 FABPL HUMAN 1.5354

GFQALGDAADIR 617.3 717.4 TIMP1 HUMAN 1.4175

TGISPLALIK 506.8 654.5 APOB HUMAN 1.3562

YTTEIIK 434.2 603.4 C1R HUMAN 1.2855

ETPEGAEAKPWYEPIYLGGVFQLEK 951.14 8 TNFA HUMAN 1.1198

77.5

ANDQYLTAAALHNLDEAVK 686.3 317.2 ILIA HUMAN 1.0574

ILPSVPK 377.2 244.2 PGH1 HUMAN 1.0282

ALDLSLK 380.2 185.1 ITIH3 HUMAN 1.0057

NAVVQGLEQPHGLWHPLR 688.4 890.6 LRP1 HUMAN 0.9884

IEGNLIFDPNNYLPK 874.0 845.5 APOB HUMAN 0.9846

ALDLSLK 380.2 575.3 ITIH3 HUMAN 0.9327

LDFHFSSDR 375.2 464.2 INHBC HUMAN 0.8852

LSIPQITTK 500.8 800.5 PSG5 HUMAN 0.7740

SERPPIFEIR 415.2 564.3 LRP1 HUMAN 0.7013

AEAQAQYSAAVAK 654.3 709.4 ITIH4 HUMAN 0.6752

IHWESASLLR 606.3 437.2 C03 HUMAN 0.6176

LFIPQITPK 528.8 261.2 PSG11 HUMAN 0.5345

FICPLTGLWPINTLK 887.0 685.4 APOH HUMAN 0.5022

DFNQFSSGEK 386.8 189.1 FETA HUMAN 0.4932

TATSEYQTFFNPR 781.4 272.2 THRB HUMAN 0.4725

SPELQAEAK 486.8 788.4 APOA2 HUMAN 0.4153

FIVGFTR 420.2 261.2 CCL20 HUMAN 0.4111

TLLPVSKPEIR 418.3 288.2 C05 HUMAN 0.3409

DIIKPDPPK 11.8 342.2 IL12B HUMAN 0.3403

DTDTGALLFIGK 625.8 217.1 PEDF HUMAN 0.3073

YTTEIIK 434.2 704.4 C1R HUMAN 0.3050

SPELQAEAK 486.8 659.4 APOA2 HUMAN 0.3047

TGISPLALIK 506.8 741.5 APOB HUMAN 0.3031

VVGGLVALR 442.3 784.5 FA12 HUMAN 0.2960

WWGGQPLWITATK 772.4 373.2 ENPP2 HUMAN 0.2498

TQILEWAAER 608.8 632.3 EGLN HUMAN 0.2342

STLFVPR 410.2 272.2 PEPD HUMAN 0.2035

DYWSTVK 449.7 347.2 APOC3 HUMAN 0.2018

WWGGQPLWITATK 772.4 929.5 ENPP2 HUMAN 0.1614

SILFLGK 389.2 201.1 THBG HUMAN 0.1593

AFQVWSDVTPLR 709.88 385.3 MMP2 HUMAN 0.1551

IQTHSTTYR 369.5 540.3 F13B HUMAN 0.1434

AFQVWSDVTPLR 709.88 347.2 MMP2 HUMAN 0.1420

LSITGTYDLK 555.8 797.4 A1AT HUMAN 0.1395

LSITGTYDLK 555.8 696.4 A1AT HUMAN 0.1294

WGAAPYR 410.7 634.3 PGRP2 HUMAN 0.1259 lAPQLSTEELVSLGEK 857.5 533.3 AFAM HUMAN 0.1222

FICPLTGLWPINTLK 887.0 756.9 APOH HUMAN 0.1153

QINSYVK 426.2 496.3 CBG HUMAN 0.1055 Transition Protein SumBestCoefs EM

TATSEYQTFFNPR 781.4 386.2 THRB HUMAN 0.0921

AFLEVNEEGSEAAASTAVVIAGR 764.4 685.4 ANT3 HUMAN 0.0800

AKPALEDLR 506.8 288.2 APOA1 HUMAN 0.0734

GPGEDFR 389.2 623.3 PTGDS HUMAN 0.0616

SLLQPNK 400.2 358.2 C08A HUMAN 0.0565

ESDTSYVSLK 564.8 347.2 CRP HUMAN 0.0497

FFQYDTWK 567.8 712.3 IGF2 HUMAN 0.0475

FSVVYAK 407.2 579.4 FETUA HUMAN 0.0437

TQIDSPLSGK 523.3 703.4 VCAM1 HUMAN 0.0401

LNIGYIEDLK 589.3 837.4 PAI2 HUMAN 0.0307

IPSNPSHR 303.2 496.3 FBLN3 HUMAN 0.0281

NEIVFPAGILQAPFYTR 968.5 456.2 ECE1 HUMAN 0.0276

TLAFVR 353.7 274.2 FA7 HUMAN 0.0220

AEAQAQYSAAVAK 654.3 908.5 ITIH4 HUMAN 0.0105

AQPVQVAEGSEPDGFWEALGGK 758.0 623.4 GELS HUMAN 0.0103

QINSYVK 426.2 610.3 CBG HUMAN 0.0080

NSDQEIDFK 548.3 409.2 S10A5 HUMAN 0.0017

[00179] Table 19. Lasso Summed Coefficients Middle-Late Combined Windows

Transition Protein SumBestCoefs ML

TQILEWAAER 608.8 761.4 EGLN HUMAN 45.0403

GDTYPAELYITGSILR 885.0 274.1 F13B HUMAN 31.4888

GEVTYTTSQVSK 650.3 750.4 EGLN HUMAN 22.3322

GEVTYTTSQVSK 650.3 913.5 EGLN HUMAN 17.0298

AVDIPGLEAATPYR 736.9 286.1 TENA HUMAN 8.6029

AVDIPGLEAATPYR 736.9 399.2 TENA HUMAN 7.9874

NEIVFPAGILQAPFYTR 968.5 357.2 ECE1 HUMAN 7.8773

ALNHLPLEYNSALYSR 621.0 696.4 C06 HUMAN 6.8534

DPNGLPPEAQK 583.3 669.4 RET4 HUMAN 5.0045

GFQALGDAADIR 617.3 717.4 TIMP1 HUMAN 4.6191

ATVVYQGER 51 1.8 652.3 APOH HUMAN 4.2522

IAQYYYTFK 598.8 395.2 F13B HUMAN 3.5721

NAVVQGLEQPHGLWHPLR 688.4 285.2 LRP1 HUMAN 3.2886

IAQYYYTFK 598.8 884.4 F13B HUMAN 2.9205

SERPPIFEIR 415.2 564.3 LRP1 HUMAN 2.4237

TLAFVR 353.7 274.2 FA7 HUMAN 2.1925

EVFSKPISWEELLQ 852.9 260.2 FA40A HUMAN 2.1591

EVFSKPISWEELLQ 852.9 376.2 FA40A HUMAN 2.1586

EFDDDTYDNDIALLQLK 1014.48 501.3 TP A HUMAN 2.0892

TLAFVR 353.7 492.3 FA7 HUMAN 2.0399

EALVPLVADHK 397.9 439.8 HGFA HUMAN 1.8856

ETLLQDFR 51 1.3 565.3 AMBP HUMAN 1.7809

ALNSIIDVYHK 424.9 661.3 S10A8 HUMAN 1.61 14

AITPPHPASQANIIFDITEGNLR 825.8 917.5 FBLN1 HUMAN 1.3423

EQLGEFYEALDCLR 871.9 747.4 A1AG1 HUMAN 1.2473 Transition Protein SumBestCoefs ML

TFLTVYWTPER 706.9 502.3 ICAM1 HUMAN 0.9851

NTVISVNPSTK_580.3_845.5 VCAM1 HUMA 0.9845

N

FLNWI 410.7 560.3 HABP2 HUMAN 0.9798

ETPEGAEAKPWYEPIYLGGVFQLEK 951.14 99 TNFA HUMAN 0.9679

0.6

NVNQSLLELHK 432.2 656.3 FRIH HUMAN 0.8280

VPLALFALNR 557.3 620.4 PEPD HUMAN 0.7851

IAPQLSTEELVSLGEK 857.5 533.3 AFAM HUMAN 0.7731

AVYEAVLR 460.8 750.4 PEPD HUMAN 0.7452

LPDTPQGLLGEAR 683.87 427.2 EGLN HUMAN 0.7145

TVQAVLTVPK 528.3 428.3 PEDF HUMAN 0.6584

YSHY ER 323.48 418.2 HABP2 HUMAN 0.5244

LLELTGPK 435.8 644.4 A1BG HUMAN 0.5072

DTDTGALLFIGK 625.8 818.5 PEDF HUMAN 0.5010

DPNGLPPEAQK 583.3 497.2 RET4 HUMAN 0.4803

AHYDLR 387.7 566.3 FETUA HUMAN 0.4693

LPN VLQEK 527.8 844.5 AFAM HUMAN 0.4640

VTGLDFIPGLHPILTLSK 641.04 771.5 LEP HUMAN 0.4584

LLELTGPK 435.8 227.2 A1BG HUMAN 0.4515

YTTEIIK 434.2 704.4 C1R HUMAN 0.4194

SSNNPHSPIVEEFQVPYNK 729.4 261.2 CIS HUMAN 0.3886

ALNHLPLEY SALYSR 621.0 538.3 C06 HUMAN 0.3405

HFQNLGK 422.2 527.2 AFAM HUMAN 0.3368

EQLGEFYEALDCLR 871.9 563.3 A1AG1 HUMAN 0.3348

TQILEWAAER 608.8 632.3 EGLN HUMAN 0.2943

ALVLELAK 428.8 672.4 INHBE HUMAN 0.2895

L SNENHGI AQR 413.5 519.8 ITIH2 HUMAN 0.2835

LPNNVLQEK 527.8 730.4 AFAM HUMAN 0.2764

DTDTGALLFIGK 625.8 217.1 PEDF HUMAN 0.2694

GDTYPAELYITGSILR 885.0 922.5 F13B HUMAN 0.2594

GPITSAAELNDPQSILLR 632.3 601.4 EGLN HUMAN 0.2388

ANLFNNIFELAGLGK 793.9 834.5 LCAP HUMAN 0.2158

SEPRPGVLLR 375.2 454.3 FA7 HUMAN 0.1921

EQSLNVSQDLDTIR 539.9 557.8 SYNE2 HUMAN 0.1836

FICPLTGLWPINTLK 887.0 685.4 APOH HUMAN 0.1806

ALNFGGIGVVVGHELTHAFDDQGR 837.1 360. ECE1 HUMAN 0.1608

2

ANDQYLTAAALHNLDEAVK 686.3 317.2 ILIA HUMAN 0.1607

AQETSGEEISK 589.8 979.5 IBP1 HUMAN 0.1598

QINSYVK 426.2 610.3 CBG HUMAN 0.1592

SILFLGK 389.2 577.4 THBG HUMAN 0.1412

DAVVYPILVEFTR 761.4 286.1 HYOU1 HUMAN 0.1298

LIEIANHVDK 384.6 683.3 ADA12 HUMAN 0.1297

LSSPAVITDK 515.8 830.5 PLMN HUMAN 0.1272

LIENGYFHPVK 439.6 343.2 F13B HUMAN 0.1176

AALAAFNAQNNGSNFQLEEISR 789.1 633.3 FETUA HUMAN 0.1160 Transition Protein SumBestCoefs ML

IQTHSTTYR 369.5 540.3 F13B HUMAN 0.1146

IPKPEASFSPR 410.2 506.3 ITIH4 HUMAN 0.1001

LLDFEFSSGR 585.8 944.4 G6PE HUMAN 0.0800

YYLQGAK 421.7 516.3 ITIH4 HUMAN 0.0793

VRPQQLVK 484.3 722.4 ITIH4 HUMAN 0.0744

GPGEDFR 389.2 322.2 PTGDS HUMAN 0.0610

ITQDAQLK 458.8 803.4 CBG HUMAN 0.0541

TATSEYQTFFNPR 781.4 272.2 THRB HUMAN 0.0511

ETLLQDFR 51 1.3 322.2 AMBP HUMAN 0.0472

YEFLNGR 449.7 293.1 PLMN HUMAN 0.0345

TLYSSSPR 455.7 696.3 IC1 HUMAN 0.0316

SLLQPNK 400.2 599.4 C08A HUMAN 0.0242

LLEVPEGR 456.8 686.4 CI S HUMAN 0.0168

GGEGTGYFVDFSVR 745.9 722.4 HRG HUMAN 0.01 10

IQTHSTTYR 369.5 627.3 F13B HUMAN 0.0046

[00180] Table 20. Random Forest SummedGini All Windows

Transition Protein SumBestGini Probability

768.5

GDTYPAELYITGSILR 885.0 274.1 F13B HUMAN 5.9580 0.9692

ATVVYQGER 511.8 751.4 APOH HUMAN 5.9313 0.9677

LDFHFSSDR 375.2 611.3 FNHBC HUMA 5.8533 0.9662

N

LDFHFSSDR 375.2 464.2 FNHBC HUMA 5.8010 0.9648

N

EVFSKPISWEELLQ 852.9 260.2 FA40A HUMAN 5.6648 0.9633

DTYVSSFPR_357.8_272.2 TCEA1 HUMA 5.6549 0.9618

N

LPDTPQGLLGEAR 683.87 427.2 EGLN HUMAN 5.3806 0.9604

FLYHK 354.2 447.2 AMBP HUMAN 5.3764 0.9589

SPELQAEAK 486.8 659.4 APOA2 HUMA 5.1896 0.9574

N

GPGEDFR 389.2 322.2 PTGDS HUMA 5.1876 0.9559

N

SGVDLADSNQK_567.3_662.3 VGFR3 HUMA 5.1159 0.9545

N

TNTNEFLIDVDK 704.85 849.5 TF HUMAN 4.7216 0.9530

FICPLTGLWPINTLK 887.0 756.9 APOH HUMAN 4.6421 0.9515

LNIGYIEDLK 589.3 950.5 PAI2 HUMAN 4.6250 0.9501

EVFSKPISWEELLQ 852.9 376.2 FA40A HUMAN 4.4215 0.9486

SYTITGLQPGTDYK 772.4 680.3 FINC HUMAN 4.4103 0.9471

TLPFSR 360.7 409.2 LYAM1 HUMA 4.2148 0.9457

N

SPELQAEAK_486.8_788.4 APOA2 HUMA 4.2081 0.9442

N

GDTYPAELYITGSILR 885.0 922.5 F13B HUMAN 4.0672 0.9427

AEIEYLEK_497.8_552.3 LYAM1 HUMA 3.9248 0.9413

N

FSLVSGWGQLLDR 493.3 403.2 FA7 HUMAN 3.9034 0.9398

FLYHK 354.2 284.2 AMBP HUMAN 3.8982 0.9383

SGVDLADSNQK 567.3 591.3 VGFR3 HUMA 3.8820 0.9369

N

LDGSTHLNIFFAK 488.3 739.4 PAPP1 HUMAN 3.8770 0.9354

HFQNLGK 422.2 527.2 AFAM HUMAN 3.7628 0.9339

IAQYYYTFK 598.8 884.4 F13B HUMAN 3.7040 0.9325

GFQALGDAADIR 617.3 717.4 TIMP1 HUMAN 3.6538 0.9310

ELPQSIVYK 538.8 417.7 FBLN3 HUMAN 3.6148 0.9295

IAQYYYTFK 598.8 395.2 F13B HUMAN 3.5820 0.9280

GSLVQASEANLQAAQDFVR 668.7 735.4 ITIH1 HUMAN 3.5283 0.9266

TLPFSR_360.7_506.3 LYAM1 HUMA 3.5064 0.9251

N

VNHVTLSQPK 374.9 244.2 B2MG HUMAN 3.5045 0.9236 lAPQLSTEELVSLGEK 857.5 533.3 AFAM HUMAN 3.4990 0.9222

VEHSDLSFSK 383.5 468.2 B2MG HUMAN 3.4514 0.9207

TQILEWAAER 608.8 761.4 EGLN HUMAN 3.4250 0.9192

AHQLAIDTYQEFEETYIPK 766.0 521.3 CSH HUMAN 3.3634 0.9178 Transition Protein SumBestGini Probability

TEFLSNYLTNVDDITLVPGTLGR 846.8 6 ENPP2_HUMAN 3.3512 0.9163 00.3

HFQNLGK 422.2 285.1 AFAM HUMAN 3.3375 0.9148

VEHSDLSFSK 383.5 234.1 B2MG HUMAN 3.3371 0.9134

TELRPGETLNVNFLLR 624.68 875.5 C03 HUMAN 3.1889 0.9119

YQISVNK 426.2 292.1 FIBB HUMAN 3.1668 0.9104

YGFYTHVFR 397.2 659.4 THRB HUMAN 3.1188 0.9075

SEPRPGVLLR 375.2 454.3 FA7 HUMAN 3.1068 0.9060

IAPQLSTEELVSLGE 857.5 333.2 AFAM HUMAN 3.0917 0.9046

ILILPSVTR 506.3 785.5 PSGx HUMAN 3.0346 0.9031

TLAFVR 353.7 492.3 FA7 HUMAN 3.0237 0.9016

AKPALEDLR_506.8_288.2 APOA1 HUMA 3.0189 0.9001

N

[00181] Table 21. Random Forest SummedGini Early Window

Transition Protein SumBestGini Probability

DTDTGALLFIGK 625.8 217.1 PEDF HUMAN 10.9040 0.9662

SYNE2 HUMA

EQSLNVSQDLDTIR 539.9 758.4 N 10.6572 0.9648

IALGGLLFPASNLR 481.3 412.3 SHBG HUMAN 10.0629 0.9633

ADA 12 HUMA

FGFGGSTDSGPIR 649.3 745.4 N 10.0449 0.9618

ETPEGAEAiCPWYEPIYLGGVFQLEK 951.

14 877.5 TNFA HUMAN 10.0286 0.9604

LPDTPQGLLGEAR 683.87 427.2 EGLN HUMAN 9.8980 0.9589

FETUA HUMA

FSVVYAK 407.2 381.2 N 9.7971 0.9574

YGIEEHGK 311.5 599.3 CXA1 HUMAN 9.7850 0.9559

GFQALGDAADIR 617.3 717.4 TIMP1 HUMAN 9.7587 0.9545

VVLSSGSGPGLDLPLVLGLPLQLK 791.5

598.4 SHBG HUMAN 9.3421 0.9530

HHGPTITAK 321.2 275.1 AMBP HUMAN 9.2728 0.9515

ALALPPLGLAPLLNLWAKPQGR 770.5 4

57.3 SHBG HUMAN 9.2431 0.9501

ADA 12 HUMA

LIEIANHVDK 384.6 498.3 N 9.1368 0.9486

AFQVWSDVTPLR 709.88 347.2 MMP2 HUMAN 8.6789 0.9471

AFQVWSDVTPLR 709.88 385.3 MMP2 HUMAN 8.6339 0.9457

ETLLQDFR 511.3 322.2 AMBP HUMAN 8.6252 0.9442

ETLLQDFR 511.3 565.3 AMBP HUMAN 8.3957 0.9427

VNHVTLSQPK 374.9 459.3 B2MG HUMAN 8.3179 0.9413

HHGPTITAK 321.2 432.3 AMBP HUMAN 8.2567 0.9398

TCEA1 HUMA

DTYVSSFPR 357.8 272.2 N 8.2028 0.9383

GGEGTGYFVDFSVR 745.9 722.4 HRG HUMAN 8.0751 0.9369

DFNQFSSGEK 386.8 189.1 FETA HUMAN 8.0401 0.9354

DVLLLVHNLPQNLTGHIWYK 791.8 883.

0 PSG7 HUMAN 7.9924 0.9339

VSEADSSNADWVTK 754.9 347.2 CFAB HUMAN 7.8630 0.9325

QGHNSVFLIK 381.6 260.2 HEMO HUMAN 7.8588 0.9310

AQETSGEEISK 589.8 979.5 IBP1 HUMAN 7.7787 0.9295

DIPHWLNPTR 416.9 600.3 PAPP1 HUMAN 7.6393 0.9280

APOA2 HUMA

SPELQAEAK 486.8 788.4 N 7.6248 0.9266

QGHNSVFLIK 381.6 520.4 HEMO HUMAN 7.6042 0.9251

LIENGYFHPVK 439.6 343.2 F13B HUMAN 7.5771 0.9236

DIIKPDPPK 511.8 342.2 IL12B HUMAN 7.5523 0.9222

VNHVTLSQPK 374.9 244.2 B2MG HUMAN 7.5296 0.9207

TELRPGETLNVNFLLR 624.68 875.5 C03 HUMAN 7.4484 0.9178

QINSYVK 426.2 496.3 CBG HUMAN 7.3266 0.9163

YNSQLLSFVR 613.8 734.5 TFR1 HUMAN 7.3262 0.9148

TVQAVLTVPK 528.3 855.5 PEDF HUMAN 7.1408 0.9134

QTLSWTVTPK 580.8 818.4 PZP HUMAN 6.9764 0.9119

DVLLLVHNLPQNLPGYFWYK 810.4 328. PSG9 HUMAN 6.9663 0.9104 Transition Protein SumBestGini Probability

2

FICPLTGLWPINTLK 887.0 756.9 APOH HUMAN 6.8924 0.9090

TSYQVYSK 488.2 397.2 CI 63 A HUMAN 6.5617 0.9075

VVLSSGSGPGLDLPLVLGLPLQL 791.5

768.5 SHBG HUMAN 6.4615 0.9060

QINSYVK 426.2 610.3 CBG HUMAN 6.4595 0.9046

LHKPGVYTR 357.5 479.3 HGFA HUMAN 6.4062 0.9031

ALVLELAK 428.8 672.4 INHBE HUMAN 6.3684 0.9016

YNSQLLSFVPv 613.8 508.3 TFR1 HUMAN 6.3628 0.9001

[00182] Table 22. Random Forest SummedGini Early-Middle Combined Windows

Transition Protein SumBestGini Probability

IALGGLLFPASNLPv 481.3 412.3 SHBG HUMAN 25.1737 0.9648

LDFHFSSDR 375.2 464.2 INHBC HUMA 25.0674 0.9633

N

LIQDAVTGLTVNGQITGDK 972.0 640.4 ITIH3 HUMAN 24.5613 0.9618

VVLSSGSGPGLDLPLVLGLPLQL 791.5 SHBG HUMAN 23.2995 0.9604 768.5

DIPHWLNPTR 416.9 600.3 PAPP1 HUMAN 22.9504 0.9589

VNHVTLSQP 374.9 459.3 B2MG HUMAN 22.2821 0.9574

QINSYVK 426.2 496.3 CBG HUMAN 22.2233 0.9559

ALALPPLGLAPLLNLWAKPQGR 770.5 2 SHBG HUMAN 22.1160 0.9545 56.2

TELRPGETLNVNFLLR 624.68 875.5 C03 HUMAN 21.9043 0.9530

ITQDAQLK 458.8 803.4 CBG HUMAN 21.8933 0.9515

IAPQLSTEELVSLGEK 857.5 533.3 AFAM HUMAN 21.4577 0.9501

QINSYVK 426.2 610.3 CBG HUMAN 21.3414 0.9486

LIQDAVTGLTVNGQITGDK 972.0 798.4 ITIH3 HUMAN 21.2843 0.9471

DTDTGALLFIGK 625.8 818.5 PEDF HUMAN 21.2631 0.9457

DVLLLVHNLPQNLPGYFWYK 810.4 328. PSG9_HUMAN 21.2547 0.9442 2

HFQNLGK 422.2 285.1 AFAM HUMAN 20.8051 0.9427

DTDTGALLFIGK 625.8 217.1 PEDF HUMAN 20.2572 0.9413

FLYHK 354.2 447.2 AMBP HUMAN 19.6822 0.9398

NNQLVAGYLQGPNVNLEEK 700.7 999.5 IL1RA HUMAN 19.2156 0.9383

VSFSSPLVAISGVALR 802.0 715.4 PAPP1 HUMAN 18.9721 0.9369

TVQAVLTVPK 528.3 428.3 PEDF HUMAN 18.9392 0.9354

TFVNITPAEVGVLVGK 822.47 968.6 PROF1 HUMAN 18.9351 0.9339

LQVLGK 329.2 416.3 A2GL HUMAN 18.6613 0.9325

TLAFVR 353.7 274.2 FA7 HUMAN 18.5095 0.9310

ITQDAQLK 458.8 702.4 CBG HUMAN 18.5046 0.9295

DVLLLVHNLPQNLTGHIWYK 791.8 310. PSG7_HUMAN 18.4015 0.9280 2

VSFSSPLVAISGVALR 802.0 602.4 PAPP1 HUMAN 17.5397 0.9266

IAPQLSTEELVSLGEK 857.5 333.2 AFAM HUMAN 17.5338 0.9251

TLFIFGVTK 513.3 215.1 PSG4 HUMAN 17.5245 0.9236

ALNFGGIGVVVGHELTHAFDDQGR 837. ECE1 HUMAN 17.1108 0.9222 1 299.2

FLYHK 354.2 284.2 AMBP HUMAN 16.9237 0.9207

LDGSTHLNIFFAK 488.3 739.4 PAPP1 HUMAN 16.8260 0.9192

ELIEELVNITQNQK 557.6 618.3 IL13 HUMAN 16.5607 0.9178

YNSQLLSFVR 613.8 734.5 TFR1 HUMAN 16.5425 0.9163

AFQVWSDVTPLR 709.88 385.3 MMP2 HUMAN 16.3293 0.9148

LDGSTHLNIFFAK 488.3 852.5 PAPP1 HUMAN 15.9820 0.9134

TPSAAYLWVGTGASEAEK 919.5 428.2 GELS HUMAN 15.9084 0.9119

YTTEIIK 434.2 603.4 C1R HUMAN 15.7998 0.9104

FSVVYAK_407.2_381.2 FETUA HUMA 15.4991 0.9090

N

VNHVTLSQPK 374.9 244.2 B2MG HUMAN 15.2938 0.9075

SYTITGLQPGTDYK 772.4 680.3 FINC HUMAN 14.9898 0.9060 Transition Protein SumBestGini Probability

DIPHWLNPTR 416.9 373.2 PAPP1 HUMAN 14.6923 0.9046

AFQVWSDVTPLR 709.88 347.2 MMP2 HUMAN 14.4361 0.9031

IAQYYYTF 598.8 884.4 F13B HUMAN 14.4245 0.9016

FSLVSGWGQLLDR 493.3 403.2 FA7 HUMAN 14.3848 0.9001

[00183] From the foregoing description, it will be apparent that variations and

[00184] The recitation of a listing of elements in any definition of a variable herein includes definitions of that variable as any single element or combination (or

[00185] All patents and publications mentioned in this specification are herein

Claims

What is claimed is:

1. A panel of isolated biomarkers comprising N of the biomarkers listed in Tables 2, 3, 4, 5 and 7 through 22.

2. The panel of claim 1 , wherein N is a number selected from the group consisting of 2 to 24.

3. The panel of claim 2, wherein said panel comprises at least two of the isolated biomarkers selected from the group consisting of FSVVYAK, SPELQAEAK,

VNHVTLSQP , SSN PHSPIVEEFQVPY K, and VVGGLVALR.

4. The panel of claim 2, wherein said panel comprises alpha- 1 -microglobulin (AMBP), ADP/ATP translocase 3 (ANT3), apolipoprotein A-II (APOA2), apolipoprotein B (APOB), apolipoprotein C-III (APOC3), beta-2-microglobulin (B2MG), complement component 1, s subcomponent (CIS), and retinol binding protein 4 (RBP4 or RET4).

5. The panel of claim 2, wherein said panel comprises at least two isolated biomarkers selected from the group consisting of alpha- 1 -microglobulin (AMBP), ADP/ATP translocase 3 (ANT3), apolipoprotein A-II (APOA2), apolipoprotein B (APOB), apolipoprotein C-III (APOC3), beta-2-microglobulin (B2MG), complement component 1, s subcomponent (CIS), and retinol binding protein 4 (RBP4 or RET4).

6. The panel of claim 2, wherein said panel comprises at least two isolated biomarkers selected from the group consisting of alpha- 1 -microglobulin (AMBP), ADP/ATP translocase 3 (ANT3), apolipoprotein A-II (APOA2), apolipoprotein B (APOB), apolipoprotein C-III (APOC3), beta-2-microglobulin (B2MG), complement component 1, s subcomponent (CIS), and retinol binding protein 4 (RBP4 or RET4) cell adhesion molecule with homology to LI CAM (CHL1), complement component C5 (C5 or C05), complement component C8 beta chain (C8B or C08B), endothelin-converting enzyme 1 (ECE1), coagulation factor XIII, B polypeptide (F13B), interleukin 5 (IL5), Peptidase D (PEPD), and plasminogen (PLM ).

7. A method of determining probability for preeclampsia in a pregnant female, the method comprising detecting a measurable feature of each of N biomarkers selected from the biomarkers listed in Tables 2, 3, 4, 5 and 7 through 22 in a biological sample obtained from said pregnant female, and analyzing said measurable features to determine the probability for preeclampsia in said pregnant female.

8. The method of claim 7, wherein said measurable feature comprises fragments or derivatives of each of said N biomarkers selected from the biomarkers listed in Tables 2, 3, 4, 5 and 7 through 22.

9. The method of claim 7, wherein said detecting a measurable feature comprises quantifying an amount of each of N biomarkers selected from the biomarkers listed in Tables 2, 3, 4, 5 and 7 through 22, combinations or portions and/or derivatives thereof in a biological sample obtained from said pregnant female.

10. The method of claim 9, further comprising calculating the probability for preeclampsia in said pregnant female based on said quantified amount of each of N biomarkers selected from the biomarkers listed in Tables 2, 3, 4, 5 and 7 through 22.

11. The method of claim 7, further comprising an initial step of providing a biomarker panel comprising N of the biomarkers listed in Tables 2, 3, 4, 5 and 7 through 22.

12. The method of claim 7, further comprising an initial step of providing a biological sample from the pregnant female.

13. The method of claim 7, further comprising communicating said probability to a health care provider.

14. The method of claim 13, wherein said communication informs a subsequent treatment decision for said pregnant female.

15. The method of claim 7, wherein N is a number selected from the group consisting of 2 to 24.

16. The method of claim 15, wherein said N biomarkers comprise at least two of the isolated biomarkers selected from the group consisting of FSVVYA , SPELQAEAK, VNHVTLSQP , SSN PHSPIVEEFQVPY K, and VVGGLVALR.

17. The method of claim 7, wherein said analysis comprises a use of a predictive model.

18. The method of claim 17, wherein said analysis comprises comparing said measurable feature with a reference feature.

19. The method of claim 18, wherein said analysis comprises using one or more selected from the group consisting of a linear discriminant analysis model, a support vector machine classification algorithm, a recursive feature elimination model, a prediction analysis of microarray model, a logistic regression model, a CART algorithm, a flex tree algorithm, a LART algorithm, a random forest algorithm, a MART algorithm, a machine learning algorithm, a penalized regression method, and a combination thereof.

20. The method of claim 19, wherein said analysis comprises logistic regression.

21. The method of claim 7, wherein said probability is expressed as a risk score.

22. The method of claim 7, wherein the biological sample is selected from the group consisting of whole blood, plasma, and serum.

23. The method of claim 22, wherein the biological sample is serum.

24. The method of claim 7, wherein said quantifying comprises mass spectrometry

(MS).

25. The method of claim 24, wherein said MS comprises liquid chromatography- mass spectrometry (LC-MS).

26. The method of claim 24, wherein said MS comprises multiple reaction monitoring (MRM) or selected reaction monitoring (SRM).

27. The method of claim 26, wherein said MRM (or SRM) comprises scheduled MRM (SRM).

28. The method of claim 7, wherein said quantifying comprises an assay that utilizes a capture agent.

29. The method of claim 28, wherein said capture agent is selected from the group consisting of and antibody, antibody fragment, nucleic acid-based protein binding reagent, small molecule or variant thereof.

30. The method of claim 28, wherein said assay is selected from the group consisting of enzyme immunoassay (EIA), enzyme-linked immunosorbent assay (ELISA), and radioimmunoassay (RIA).

31. The method of claim 30, wherein said quantifying further comprises mass spectrometry (MS).

32. The method of claim 31 , wherein said quantifying comprises co- immunoprecitipation-mass spectrometry (co-IP MS).

33. The method of claim 7, further comprising detecting a measurable feature for one or more risk indicia.

34. The method of claim 33, wherein the one or more risk indicia are selected from the group consisting of history of preeclampsia, first pregnancy, age, obesity, diabetes, gestational diabetes, hypertension, kidney disease, multiple pregnancy, interval between pregnancies, new paternity, migraine headaches, rheumatoid arthritis, and lupus.

35. A method of determining probability for preeclampsia in a pregnant female, the method comprising: (a) quantifying in a biological sample obtained from said pregnant female an amount of each of N biomarkers selected from the biomarkers listed in Tables 2, 3, 4, 5 and 7 through 22; (b) multiplying said amount by a predetermined coefficient, (c) determining the probability for preeclampsia in said pregnant female comprising adding said individual products to obtain a total risk score that corresponds to said probability.

36. The panel of claim 2, wherein said panel comprises at least two of the isolated biomarkers selected from the group consisting of LDFHFSSDR, TVQAVLTVPK,

GPGEDFR, ETLLQDFR, ATVVYQGER, and GFQALGDAADIR.

37. The panel of claim 2, wherein said panel comprises at least two of Inhibin beta C chain (F HBC), Pigment epithelium-derived factor (PEDF), Prostaglandin-H2 D- isomerase (PTGDS), alpha- 1 -microglobulin (AMBP), Beta-2-glycoprotein 1 (APOH), Metalloproteinase inhibitor 1 (TIMP1), Coagulation factor XIII B chain (F13B), Alpha-2- HS-glycoprotein (FETUA), and Sex hormone-binding globulin (SHBG).

38. The panel of claim 2, wherein said panel comprises at least two isolated biomarkers selected from the group consisting of alpha- 1 -microglobulin (AMBP), ADP/ATP translocase 3 (ANT3), apolipoprotein A-II (APOA2), apolipoprotein B (APOB),

apolipoprotein C-III (APOC3), beta-2-microglobulin (B2MG), complement component 1, s subcomponent (CIS), and retinol binding protein 4 (RBP4 or RET4) cell adhesion molecule with homology to LI CAM (CHL1), complement component C5 (C5 or C05), complement component C8 beta chain (C8B or C08B), endothelin-converting enzyme 1 (ECE1), coagulation factor XIII, B polypeptide (F13B), interleukin 5 (IL5), Peptidase D (PEPD), plasminogen (PLMN), of Inhibin beta C chain (INHBC), Pigment epithelium-derived factor (PEDF), Prostaglandin-H2 D-isomerase (PTGDS), alpha- 1 -microglobulin (AMBP), Beta-2- glycoprotein 1 (APOH), Metalloproteinase inhibitor 1 (TIMP1), Coagulation factor XIII B chain (F13B), Alpha-2-HS-glycoprotein (FETUA), and Sex hormone -binding globulin (SHBG).

39. The method of claim 7, wherein said N biomarkers comprise at least two of the isolated biomarkers selected from the group consisting of LDFHFSSDR,

TVQAVLTVPK, GPGEDFR, ETLLQDFR, ATVVYQGER, and GFQALGDAADIR.

40. The method of claim 7, wherein said N biomarkers comprise at least two of Inhibin beta C chain (INHBC), Pigment epithelium-derived factor (PEDF), Prostaglandin-H2 D-isomerase (PTGDS), alpha- 1 -microglobulin (AMBP), Beta-2-glycoprotein 1 (APOH), Metalloproteinase inhibitor 1 (TEMPI), Coagulation factor XIII B chain (F13B), Alpha-2- HS-glycoprotein (FETUA), and Sex hormone -binding globulin (SHBG).

41. The method of claim 7, wherein said N biomarkers comprise at least two isolated biomarkers selected from the group consisting of alpha- 1 -microglobulin (AMBP), ADP/ATP translocase 3 (ANT3), apolipoprotein A-II (APOA2), apolipoprotein B (APOB), apolipoprotein C-III (APOC3), beta-2-microglobulin (B2MG), complement component 1, s subcomponent (CIS), and retinol binding protein 4 (RBP4 or RET4) cell adhesion molecule with homology to LI CAM (CHL1), complement component C5 (C5 or C05), complement component C8 beta chain (C8B or C08B), endothelin-converting enzyme 1 (ECE1), coagulation factor XIII, B polypeptide (F13B), interleukin 5 (IL5), Peptidase D (PEPD), plasminogen (PLMN), of Inhibin beta C chain (INHBC), Pigment epithelium-derived factor (PEDF), Prostaglandin-H2 D-isomerase (PTGDS), alpha- 1 -microglobulin (AMBP), Beta-2- glycoprotein 1 (APOH), Metalloproteinase inhibitor 1 (TIMP1), Coagulation factor XIII B chain (F13B), Alpha-2-HS-glycoprotein (FETUA), and Sex hormone-binding globulin (SHBG).

42. The method of claim 35 wherein said N biomarkers comprise at least two of the isolated biomarkers selected from the group consisting of LDFHFSSD ,

TVQAVLTVPK, GPGEDFR, ETLLQDFR, ATVVYQGER, and GFQALGDAADIR.

43. The method of claim 35 wherein said N biomarkers comprise at least two of Inhibin beta C chain (INHBC), Pigment epithelium-derived factor (PEDF), Prostaglandin-H2 D-isomerase (PTGDS), alpha- 1 -microglobulin (AMBP), Beta-2-glycoprotein 1 (APOH), Metalloproteinase inhibitor 1 (TIMP1), Coagulation factor XIII B chain (F13B), Alpha-2- HS-glycoprotein (FETUA), and Sex hormone -binding globulin (SHBG).

44. The method of claim 35, herein said N biomarkers comprise at least two isolated biomarkers selected from the group consisting of alpha- 1 -microglobulin (AMBP), ADP/ATP translocase 3 (ANT3), apolipoprotein A-II (APOA2), apolipoprotein B (APOB), apolipoprotein C-III (APOC3), beta-2-microglobulin (B2MG), complement component 1, s subcomponent (CIS), and retinol binding protein 4 (RBP4 or RET4) cell adhesion molecule with homology to LI CAM (CHL1), complement component C5 (C5 or C05), complement component C8 beta chain (C8B or C08B), endothelin-converting enzyme 1 (ECE1), coagulation factor XIII, B polypeptide (F13B), interleukin 5 (IL5), Peptidase D (PEPD), plasminogen (PLMN), of Inhibin beta C chain (INHBC), Pigment epithelium-derived factor (PEDF), Prostaglandin-H2 D-isomerase (PTGDS), alpha- 1 -microglobulin (AMBP), Beta-2- glycoprotein 1 (APOH), Metalloproteinase inhibitor 1 (TIMP1), Coagulation factor XIII B chain (F13B), Alpha-2-HS-glycoprotein (FETUA), and Sex hormone -binding globulin (SHBG).